Medical image segmentation is a key step to assist diagnosis of several diseases, and accuracy of... more Medical image segmentation is a key step to assist diagnosis of several diseases, and accuracy of a segmentation method is important for further treatments of different diseases. Different medical imaging modalities have different challenges such as intensity inhomogeneity, noise, low contrast, and ill-defined boundaries, which make automated segmentation a difficult task. To handle these issues, we propose a new fully automated method for medical image segmentation, which utilizes the advantages of thresholding and an active contour model. In this study, a Harris Hawks optimizer is applied to determine the optimal thresholding value, which is used to obtain the initial contour for segmentation. The obtained contour is further refined by using a spatially varying Gaussian kernel in the active contour model. The proposed method is then validated using a standard skin dataset (ISBI 2016), which consists of variable-sized lesions and different challenging artifacts, and a standard card...
There has always been a need to solve real-life large-scale problems, suchas efficiently allocati... more There has always been a need to solve real-life large-scale problems, suchas efficiently allocating limited resources, and other complex and conflicting situations related to combinatorial optimiza ...
Advanced Analytics and Learning on Temporal Data, 2021
With the ever-growing volume of online news feeds, event-based organization of news articles has ... more With the ever-growing volume of online news feeds, event-based organization of news articles has many practical applications including better information navigation and the ability to view and analyze events as they develop. Automatically tracking the evolution of events in large news corpora still remains a challenging task, and the existing techniques for Event Detection and Tracking do not place a particular focus on tracking events in very large and constantly updating news feeds. Here, we propose a new method for robust and efficient event detection and tracking, which we call RevDet algorithm. RevDet adopts an iterative clustering approach for tracking events. Even though many events continue to develop for many days or even months, RevDet is able to detect and track those events while utilizing only a constant amount of space on main memory. We also devise a redundancy removal strategy which effectively eliminates duplicate news articles and substantially reduces the size of data. We construct a large, comprehensive new ground truth dataset specifically for event detection and tracking approaches by augmenting two existing datasets: w2e and GDELT. We implement RevDet algorithm and evaluate its performance on the ground truth event chains. We discover that our algorithm is able to accurately recover event chains in the ground-truth dataset. We also compare the memory efficiency of our algorithm with the standard single pass clustering approach, and demonstrate the appropriateness of our algorithm for event detection and tracking task in large news feeds.
Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 2020
Sine Cosine Algorithm (SCA) is one of the highly-referred optimization algorithms in the literatu... more Sine Cosine Algorithm (SCA) is one of the highly-referred optimization algorithms in the literature. The present study contributes by discovering three shortcomings of SCA and making a few recommendations. We show that the mathematical model of SCA does not work as explained in the original paper and its performance can be improved by modifying the position-updating equation of SCA as stated in the original paper. Moreover, we empirically and statistically show that sine and cosine functions, which make this algorithm different from the others, can be replaced with a simple random variable having a value in the range [−1, 1] without degrading the overall performance of the algorithm. Furthermore, we demonstrate that the behavior of SCA is biased for the functions having the global optimum at the origin. Finally, on the basis of the analysis of SCA, we make two recommendations for the metaheuristics designers regarding the selection of the benchmarks and mapping of the inspiration when designing a new algorithm.
To solve global optimization problems, nature-inspired metaheuristics have emerged as the best op... more To solve global optimization problems, nature-inspired metaheuristics have emerged as the best option in many cases. The researchers have mimicked or mapped the optimal behaviors of living organisms, natural phenomena and human behaviors to solve optimization problems. Whenever a new algorithm is proposed, its exploration, exploitation and convergence capabilities are tested; however, in this study, it is highlighted that a metaheuristic should also exhibit invariance to function translation/shifting. Whale optimization algorithm (WOA) is a well-reputed and highly referred algorithm in the literature; however, we find that its performance is strongly affected under certain circumstances involving function shifting. Our analysis reveals that WOA performs really well when the global optimum is situated at the origin ( $$0, 0, \ldots , 0$$ ); on the contrary, the performance amazingly degrades whenever the global optimum is situated away from the origin. Moreover, in most cases, our findings reveal that the performance degradation is directly proportional to the distance of the global optimum from the origin. Furthermore, the root cause of the problem is discovered and discussed. Finally, it is suggested how WOA can be made shift-invariant.
Designing reliable sequences of DNA (Deoxyribonucleic Acid) is a critical task in the fields of D... more Designing reliable sequences of DNA (Deoxyribonucleic Acid) is a critical task in the fields of DNA computing, and nanotechnology. The quality and reliability of the DNA sequence can directly affect the accuracy of the processing of information stored in sequences. This problem of designing reliable sequences belongs to the NP-hard class of problems. It has many incompatible design criteria, which cannot be optimized at the same time. Many objective evolutionary algorithms can balance conflicting design criteria by using a diverse population of solutions. This paper proposes an opposition-based Memetic Generalized Differential Evolution (MGDE3) to handle four conflicting design criteria for reliable DNA sequence design. Opposition-based learning and local search strategies are suggested to strengthen the explorative and exploitative properties of the proposed MGDE3. The proposed algorithm is bench-marked with small, medium, and large data sets against 7 highly-cited many-objective and multi-objective algorithms. Experimental results and statistical analysis reveal that MGDE3 significantly outperforms the compared algorithms. The proposed method generates reliable real-life sequences of DNA that are substantially better than the DNA sequences generated by other considered algorithms.
Abstract Political Optimizer (PO) is a recently proposed meta-heuristic with excellent convergenc... more Abstract Political Optimizer (PO) is a recently proposed meta-heuristic with excellent convergence speed and exploitation capability. However, it is found that PO prematurely converges for complex problems because of not giving enough time to the exploration. In this paper, the exploration capability and balance of PO are improved by making multiple modifications to propose an Improved Political Optimizer (IPO). To improve the exploration capability, the condition of an equal number of parties and constituencies is relaxed, and switching with a random member of a random party is incorporated in the party-switching phase. Moreover, the balance between exploration and exploitation is enhanced by modifying the position-updating strategy (RPPUS) in the election campaign phase and replacing the tunable party-switching rate with a self-adaptive parameter. The exploitation is further improved by utilizing the best solution of the population in the parliamentary affairs phase. In addition to improvement in PO, this paper also highlights a correction in the party-switching phase of the original PO. The performance of IPO is evaluated using 30 CEC-2014 benchmarks, 29 CEC-BC-2017 benchmarks, and 6 mechanical engineering problems. It is shown through non-parametric statistical Wilcoxon’s rank-sum test that IPO significantly outperforms PO. Moreover, IPO is also compared with 10 of the well-cited and 14 latest optimization algorithms published in 2020. It is shown by using the Friedman mean-rank test that IPO secures the first rank for both types of benchmark functions. Moreover, the comparison of IPO with PO and a few well-known algorithms for 6 of the engineering problems shows that IPO performs better or equivalently to the compared optimization algorithms.
Political optimizer (PO) is a recently proposed human-behavior inspired meta-heuristic, which has... more Political optimizer (PO) is a recently proposed human-behavior inspired meta-heuristic, which has shown tremendous performance on complex multimodal functions as well as engineering optimization problems. Good convergence speed and well-balanced exploratory and exploitative behavior of PO convince us to employ PO for the training of feedforward neural network (FNN). The FNN-training problem is formulated as an optimization problem in which the objective is to minimize the mean squared error (MSE) or cross entropy (CE). The weights and biases of the FNN are arranged in the form of a vector called a candidate solution. The performance of the proposed trainer is evaluated on 5 classification data-sets and 5 function-approximation data-sets, which have already been used in the literature. In recent years, grey wolf optimizer, moth flame optimization, multi-verse optimizer, sine-cosine algorithm, whale optimization algorithm, ant lion optimizer, and Salp swarm algorithm have successfully been applied on neural network training. In this paper, we compare the performance of PO with these algorithms and show that PO either outperforms them or performs equivalently. The MSE, CE, training set accuracy, and test set accuracy are used as metrics for the comparative analysis. The non-parametric Wilcoxon’s rank-sum test is used to show the statistical significance of the results. Based on the tremendous performance, we highly recommend using PO for the training of artificial neural networks to solve the classification and regression problems.
Abstract Convolution autoencoders have shown that they have useful applications in multiple areas... more Abstract Convolution autoencoders have shown that they have useful applications in multiple areas related to image classification and generation. The architecture of deep neural networks has a huge impact on the performance of the network. In recent years, metaheuristics have become quite popular for the optimization of the architectures of deep neural networks. In this paper, we propose a multi-objective Particle Swarm Optimization (PSO) for designing flexible convolution autoencoders by evolving the arrangement of convolution and pooling layers along with the number of parameters. A novel encoding strategy for PSO particles is introduced, which allows flexible positioning of pooling layers. To enhance the exploration of the search space, velocity and the position update methods for variable-length particles in PSO are also modified. The proposed method is evaluated on different datasets for image classification. The experimental results show that the resultant architectures are more generalized, optimized, and accurate than their competitors.
In many objective optimization problems (MaOPs), more than three distinct objectives are optimize... more In many objective optimization problems (MaOPs), more than three distinct objectives are optimized. The challenging part in MaOPs is to get the Pareto approximation (PA) with high diversity and good convergence. In Literature, in order to solve the issue of diversity and convergence in MaOPs, many approaches are proposed using different multi objective evolutionary algorithms (MOEAs). Moreover, to get better results, the researchers use the sets of reference points to differentiate the solutions and to model the search process, it further evaluates and selects the non-dominating solutions by using the reference set of solutions. Furthermore, this technique is used in some of the swarm-based evolutionary algorithms. In this paper, we have used some effective adaptations of bat algorithm with the previous mentioned approach to effectively handle the many objective problems. Moreover, we have called this algorithm as many objective bat algorithm (MaOBAT). This algorithm is a biologically inspired algorithm, which uses echolocation power of micro bats. Each bat represents a complete solution, which can be evaluated based on the problem specific fitness function and then based on the dominance relationship, non-dominated solutions are selected. In proposed MaOBAT, dominance rank is used as dominance relationship (dominance rank of a solution means by how many other solutions a solution dominated). In our proposed strategy, dynamically allocated set of reference points are used, allowing the algorithm to have good convergence and high diversity pareto fronts (PF). The experimental results show that the proposed algorithm has significant advantages over several state-of-the-art algorithms in terms of the quality of the solution.
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
International Journal on Artificial Intelligence Tools, 2020
The pickup and delivery problem (PDP) is a very common and important problem, which has a large n... more The pickup and delivery problem (PDP) is a very common and important problem, which has a large number of real-world applications in logistics and transportation. In PDP, customers send transportation requests to pick up an object from one place and deliver it to another place. This problem is under the focus of researchers since the last two decades with multiple variations. In the literature, different variations of PDP with different number of objectives and constraints have been considered. Depending on the number of objectives, multi and many-objective evolutionary algorithms have been applied to solve the problem and to study the conflicts between objectives. In this paper, PDP is formulated as a many-objective pickup and delivery problem (MaOPDP) with delay time of vehicle having six criteria to be optimized. To the best of our knowledge, this variation of PDP has not been considered in the literature. To solve the problem, this paper proposes a memetic I-DBEA (Improved Decom...
International Journal of Pattern Recognition and Artificial Intelligence, 2019
Evolutionary machine learning research aims to develop classifier systems that can solve complex ... more Evolutionary machine learning research aims to develop classifier systems that can solve complex and hard tasks. This paper addresses the problem of inferring a regular expression from a given set of strings for automating the task of information extraction. To the best of our knowledge, this paper is the first to propose the extension of accuracy-based classifier system XCS to learn the regular expressions for text extraction. This new system named as XCSREA includes tree-like code fragments to learn regular expressions. The genetic algorithm in action sets uses two-point crossover with uniform mutation and Roulette wheel parent selection method. Seven different datasets, each with three different lengths, are used to compare the performance of the proposed model with standard genetic programming (GP) approach. The experimental results demonstrate that XCSREA outperforms standard GP approach when sufficiently large numbers of classifiers are used.
The problem of optimally assigning agents (resources) to a given set of tasks is known as the ass... more The problem of optimally assigning agents (resources) to a given set of tasks is known as the assignment problem (AP). The classical AP and many of its variations have been extensively discussed in the literature. In this paper, we examine a specific class of the problem, in which each task is assigned to a group of collaborating agents. APs in this class cannot be solved using the Hungarian or other known polynomial time algorithms. We employ the genetic algorithm (GA) to solve the problem. However, we show that if the size of the problem is large, then standard crossover operators cannot efficiently find nearoptimal solutions within a reasonable time. In general, the efficiency of the GA depends on the choice of genetic operators (selection, crossover, and mutation) and the associated parameters. In order to design an efficient GA for determining the near-optimal assignment of tasks to collaborative agents, we focus on the construction of crossover operators. We analyze why a naive implementation with standard crossover operators is not capable of sufficiently solving the problem. Furthermore, we suggest modifications to these operators by adding a shuffled list and introduce two new operators (team-based and team-based shuffled list). We demonstrate that the modified and new operators with shuffled lists perform significantly better than all operators without shuffled lists and solve the presented AP more efficiently. The performance of the GA can be further enhanced by using chaotic sequences. Moreover, the GA is also compared with the particle swarm optimization (PSO) and differential evolution (DE) algorithms, demonstrating the superiority of the GA over these search algorithms.
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation, 2013
ABSTRACT A highly interesting but not thoroughly addressed optimization problem is a variation of... more ABSTRACT A highly interesting but not thoroughly addressed optimization problem is a variation of the Assignment Problem (AP) where tasks are assigned to groups of collaborating agents (teams). In this paper, we address this class of AP as a bi-objective optimization problem, in which the cost is minimized and the quality is maximized. To solve the model, we adopt Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Strength Pareto Evolutionary Algorithm 2 (SPEA2). We conduct several experiments on problems with varying sizes to compare the NSGA-II and SPEA2 algorithms.
Communications in Computer and Information Science, 2013
In this paper, we consider the problem of rescheduling human resources in a battalion where new a... more In this paper, we consider the problem of rescheduling human resources in a battalion where new activities are assigned to the battalion by higher headquarters, requiring modification of an existing original schedule. The problem is modeled as a multi-criteria optimization problem with three objectives: (i) maximizing the number of tasks that are performed, (ii) minimizing the number of high-priority tasks that are missed, and (iii) minimizing the differences between the original schedule and the modified one. In order to solve the optimization model, we adopt Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The accuracy of NSGA-II in this context is verified by considering a small-sized problem where it is easy to verify solutions. Furthermore, we consider a realistic problem instance for a battalion with 400 agents and 66 tasks in the initial schedule. We present the computational results of rescheduling when unpredictable activities emerge.
Medical image segmentation is a key step to assist diagnosis of several diseases, and accuracy of... more Medical image segmentation is a key step to assist diagnosis of several diseases, and accuracy of a segmentation method is important for further treatments of different diseases. Different medical imaging modalities have different challenges such as intensity inhomogeneity, noise, low contrast, and ill-defined boundaries, which make automated segmentation a difficult task. To handle these issues, we propose a new fully automated method for medical image segmentation, which utilizes the advantages of thresholding and an active contour model. In this study, a Harris Hawks optimizer is applied to determine the optimal thresholding value, which is used to obtain the initial contour for segmentation. The obtained contour is further refined by using a spatially varying Gaussian kernel in the active contour model. The proposed method is then validated using a standard skin dataset (ISBI 2016), which consists of variable-sized lesions and different challenging artifacts, and a standard card...
There has always been a need to solve real-life large-scale problems, suchas efficiently allocati... more There has always been a need to solve real-life large-scale problems, suchas efficiently allocating limited resources, and other complex and conflicting situations related to combinatorial optimiza ...
Advanced Analytics and Learning on Temporal Data, 2021
With the ever-growing volume of online news feeds, event-based organization of news articles has ... more With the ever-growing volume of online news feeds, event-based organization of news articles has many practical applications including better information navigation and the ability to view and analyze events as they develop. Automatically tracking the evolution of events in large news corpora still remains a challenging task, and the existing techniques for Event Detection and Tracking do not place a particular focus on tracking events in very large and constantly updating news feeds. Here, we propose a new method for robust and efficient event detection and tracking, which we call RevDet algorithm. RevDet adopts an iterative clustering approach for tracking events. Even though many events continue to develop for many days or even months, RevDet is able to detect and track those events while utilizing only a constant amount of space on main memory. We also devise a redundancy removal strategy which effectively eliminates duplicate news articles and substantially reduces the size of data. We construct a large, comprehensive new ground truth dataset specifically for event detection and tracking approaches by augmenting two existing datasets: w2e and GDELT. We implement RevDet algorithm and evaluate its performance on the ground truth event chains. We discover that our algorithm is able to accurately recover event chains in the ground-truth dataset. We also compare the memory efficiency of our algorithm with the standard single pass clustering approach, and demonstrate the appropriateness of our algorithm for event detection and tracking task in large news feeds.
Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion, 2020
Sine Cosine Algorithm (SCA) is one of the highly-referred optimization algorithms in the literatu... more Sine Cosine Algorithm (SCA) is one of the highly-referred optimization algorithms in the literature. The present study contributes by discovering three shortcomings of SCA and making a few recommendations. We show that the mathematical model of SCA does not work as explained in the original paper and its performance can be improved by modifying the position-updating equation of SCA as stated in the original paper. Moreover, we empirically and statistically show that sine and cosine functions, which make this algorithm different from the others, can be replaced with a simple random variable having a value in the range [−1, 1] without degrading the overall performance of the algorithm. Furthermore, we demonstrate that the behavior of SCA is biased for the functions having the global optimum at the origin. Finally, on the basis of the analysis of SCA, we make two recommendations for the metaheuristics designers regarding the selection of the benchmarks and mapping of the inspiration when designing a new algorithm.
To solve global optimization problems, nature-inspired metaheuristics have emerged as the best op... more To solve global optimization problems, nature-inspired metaheuristics have emerged as the best option in many cases. The researchers have mimicked or mapped the optimal behaviors of living organisms, natural phenomena and human behaviors to solve optimization problems. Whenever a new algorithm is proposed, its exploration, exploitation and convergence capabilities are tested; however, in this study, it is highlighted that a metaheuristic should also exhibit invariance to function translation/shifting. Whale optimization algorithm (WOA) is a well-reputed and highly referred algorithm in the literature; however, we find that its performance is strongly affected under certain circumstances involving function shifting. Our analysis reveals that WOA performs really well when the global optimum is situated at the origin ( $$0, 0, \ldots , 0$$ ); on the contrary, the performance amazingly degrades whenever the global optimum is situated away from the origin. Moreover, in most cases, our findings reveal that the performance degradation is directly proportional to the distance of the global optimum from the origin. Furthermore, the root cause of the problem is discovered and discussed. Finally, it is suggested how WOA can be made shift-invariant.
Designing reliable sequences of DNA (Deoxyribonucleic Acid) is a critical task in the fields of D... more Designing reliable sequences of DNA (Deoxyribonucleic Acid) is a critical task in the fields of DNA computing, and nanotechnology. The quality and reliability of the DNA sequence can directly affect the accuracy of the processing of information stored in sequences. This problem of designing reliable sequences belongs to the NP-hard class of problems. It has many incompatible design criteria, which cannot be optimized at the same time. Many objective evolutionary algorithms can balance conflicting design criteria by using a diverse population of solutions. This paper proposes an opposition-based Memetic Generalized Differential Evolution (MGDE3) to handle four conflicting design criteria for reliable DNA sequence design. Opposition-based learning and local search strategies are suggested to strengthen the explorative and exploitative properties of the proposed MGDE3. The proposed algorithm is bench-marked with small, medium, and large data sets against 7 highly-cited many-objective and multi-objective algorithms. Experimental results and statistical analysis reveal that MGDE3 significantly outperforms the compared algorithms. The proposed method generates reliable real-life sequences of DNA that are substantially better than the DNA sequences generated by other considered algorithms.
Abstract Political Optimizer (PO) is a recently proposed meta-heuristic with excellent convergenc... more Abstract Political Optimizer (PO) is a recently proposed meta-heuristic with excellent convergence speed and exploitation capability. However, it is found that PO prematurely converges for complex problems because of not giving enough time to the exploration. In this paper, the exploration capability and balance of PO are improved by making multiple modifications to propose an Improved Political Optimizer (IPO). To improve the exploration capability, the condition of an equal number of parties and constituencies is relaxed, and switching with a random member of a random party is incorporated in the party-switching phase. Moreover, the balance between exploration and exploitation is enhanced by modifying the position-updating strategy (RPPUS) in the election campaign phase and replacing the tunable party-switching rate with a self-adaptive parameter. The exploitation is further improved by utilizing the best solution of the population in the parliamentary affairs phase. In addition to improvement in PO, this paper also highlights a correction in the party-switching phase of the original PO. The performance of IPO is evaluated using 30 CEC-2014 benchmarks, 29 CEC-BC-2017 benchmarks, and 6 mechanical engineering problems. It is shown through non-parametric statistical Wilcoxon’s rank-sum test that IPO significantly outperforms PO. Moreover, IPO is also compared with 10 of the well-cited and 14 latest optimization algorithms published in 2020. It is shown by using the Friedman mean-rank test that IPO secures the first rank for both types of benchmark functions. Moreover, the comparison of IPO with PO and a few well-known algorithms for 6 of the engineering problems shows that IPO performs better or equivalently to the compared optimization algorithms.
Political optimizer (PO) is a recently proposed human-behavior inspired meta-heuristic, which has... more Political optimizer (PO) is a recently proposed human-behavior inspired meta-heuristic, which has shown tremendous performance on complex multimodal functions as well as engineering optimization problems. Good convergence speed and well-balanced exploratory and exploitative behavior of PO convince us to employ PO for the training of feedforward neural network (FNN). The FNN-training problem is formulated as an optimization problem in which the objective is to minimize the mean squared error (MSE) or cross entropy (CE). The weights and biases of the FNN are arranged in the form of a vector called a candidate solution. The performance of the proposed trainer is evaluated on 5 classification data-sets and 5 function-approximation data-sets, which have already been used in the literature. In recent years, grey wolf optimizer, moth flame optimization, multi-verse optimizer, sine-cosine algorithm, whale optimization algorithm, ant lion optimizer, and Salp swarm algorithm have successfully been applied on neural network training. In this paper, we compare the performance of PO with these algorithms and show that PO either outperforms them or performs equivalently. The MSE, CE, training set accuracy, and test set accuracy are used as metrics for the comparative analysis. The non-parametric Wilcoxon’s rank-sum test is used to show the statistical significance of the results. Based on the tremendous performance, we highly recommend using PO for the training of artificial neural networks to solve the classification and regression problems.
Abstract Convolution autoencoders have shown that they have useful applications in multiple areas... more Abstract Convolution autoencoders have shown that they have useful applications in multiple areas related to image classification and generation. The architecture of deep neural networks has a huge impact on the performance of the network. In recent years, metaheuristics have become quite popular for the optimization of the architectures of deep neural networks. In this paper, we propose a multi-objective Particle Swarm Optimization (PSO) for designing flexible convolution autoencoders by evolving the arrangement of convolution and pooling layers along with the number of parameters. A novel encoding strategy for PSO particles is introduced, which allows flexible positioning of pooling layers. To enhance the exploration of the search space, velocity and the position update methods for variable-length particles in PSO are also modified. The proposed method is evaluated on different datasets for image classification. The experimental results show that the resultant architectures are more generalized, optimized, and accurate than their competitors.
In many objective optimization problems (MaOPs), more than three distinct objectives are optimize... more In many objective optimization problems (MaOPs), more than three distinct objectives are optimized. The challenging part in MaOPs is to get the Pareto approximation (PA) with high diversity and good convergence. In Literature, in order to solve the issue of diversity and convergence in MaOPs, many approaches are proposed using different multi objective evolutionary algorithms (MOEAs). Moreover, to get better results, the researchers use the sets of reference points to differentiate the solutions and to model the search process, it further evaluates and selects the non-dominating solutions by using the reference set of solutions. Furthermore, this technique is used in some of the swarm-based evolutionary algorithms. In this paper, we have used some effective adaptations of bat algorithm with the previous mentioned approach to effectively handle the many objective problems. Moreover, we have called this algorithm as many objective bat algorithm (MaOBAT). This algorithm is a biologically inspired algorithm, which uses echolocation power of micro bats. Each bat represents a complete solution, which can be evaluated based on the problem specific fitness function and then based on the dominance relationship, non-dominated solutions are selected. In proposed MaOBAT, dominance rank is used as dominance relationship (dominance rank of a solution means by how many other solutions a solution dominated). In our proposed strategy, dynamically allocated set of reference points are used, allowing the algorithm to have good convergence and high diversity pareto fronts (PF). The experimental results show that the proposed algorithm has significant advantages over several state-of-the-art algorithms in terms of the quality of the solution.
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
This is a PDF file of an article that has undergone enhancements after acceptance, such as the ad... more This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
International Journal on Artificial Intelligence Tools, 2020
The pickup and delivery problem (PDP) is a very common and important problem, which has a large n... more The pickup and delivery problem (PDP) is a very common and important problem, which has a large number of real-world applications in logistics and transportation. In PDP, customers send transportation requests to pick up an object from one place and deliver it to another place. This problem is under the focus of researchers since the last two decades with multiple variations. In the literature, different variations of PDP with different number of objectives and constraints have been considered. Depending on the number of objectives, multi and many-objective evolutionary algorithms have been applied to solve the problem and to study the conflicts between objectives. In this paper, PDP is formulated as a many-objective pickup and delivery problem (MaOPDP) with delay time of vehicle having six criteria to be optimized. To the best of our knowledge, this variation of PDP has not been considered in the literature. To solve the problem, this paper proposes a memetic I-DBEA (Improved Decom...
International Journal of Pattern Recognition and Artificial Intelligence, 2019
Evolutionary machine learning research aims to develop classifier systems that can solve complex ... more Evolutionary machine learning research aims to develop classifier systems that can solve complex and hard tasks. This paper addresses the problem of inferring a regular expression from a given set of strings for automating the task of information extraction. To the best of our knowledge, this paper is the first to propose the extension of accuracy-based classifier system XCS to learn the regular expressions for text extraction. This new system named as XCSREA includes tree-like code fragments to learn regular expressions. The genetic algorithm in action sets uses two-point crossover with uniform mutation and Roulette wheel parent selection method. Seven different datasets, each with three different lengths, are used to compare the performance of the proposed model with standard genetic programming (GP) approach. The experimental results demonstrate that XCSREA outperforms standard GP approach when sufficiently large numbers of classifiers are used.
The problem of optimally assigning agents (resources) to a given set of tasks is known as the ass... more The problem of optimally assigning agents (resources) to a given set of tasks is known as the assignment problem (AP). The classical AP and many of its variations have been extensively discussed in the literature. In this paper, we examine a specific class of the problem, in which each task is assigned to a group of collaborating agents. APs in this class cannot be solved using the Hungarian or other known polynomial time algorithms. We employ the genetic algorithm (GA) to solve the problem. However, we show that if the size of the problem is large, then standard crossover operators cannot efficiently find nearoptimal solutions within a reasonable time. In general, the efficiency of the GA depends on the choice of genetic operators (selection, crossover, and mutation) and the associated parameters. In order to design an efficient GA for determining the near-optimal assignment of tasks to collaborative agents, we focus on the construction of crossover operators. We analyze why a naive implementation with standard crossover operators is not capable of sufficiently solving the problem. Furthermore, we suggest modifications to these operators by adding a shuffled list and introduce two new operators (team-based and team-based shuffled list). We demonstrate that the modified and new operators with shuffled lists perform significantly better than all operators without shuffled lists and solve the presented AP more efficiently. The performance of the GA can be further enhanced by using chaotic sequences. Moreover, the GA is also compared with the particle swarm optimization (PSO) and differential evolution (DE) algorithms, demonstrating the superiority of the GA over these search algorithms.
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation, 2013
ABSTRACT A highly interesting but not thoroughly addressed optimization problem is a variation of... more ABSTRACT A highly interesting but not thoroughly addressed optimization problem is a variation of the Assignment Problem (AP) where tasks are assigned to groups of collaborating agents (teams). In this paper, we address this class of AP as a bi-objective optimization problem, in which the cost is minimized and the quality is maximized. To solve the model, we adopt Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Strength Pareto Evolutionary Algorithm 2 (SPEA2). We conduct several experiments on problems with varying sizes to compare the NSGA-II and SPEA2 algorithms.
Communications in Computer and Information Science, 2013
In this paper, we consider the problem of rescheduling human resources in a battalion where new a... more In this paper, we consider the problem of rescheduling human resources in a battalion where new activities are assigned to the battalion by higher headquarters, requiring modification of an existing original schedule. The problem is modeled as a multi-criteria optimization problem with three objectives: (i) maximizing the number of tasks that are performed, (ii) minimizing the number of high-priority tasks that are missed, and (iii) minimizing the differences between the original schedule and the modified one. In order to solve the optimization model, we adopt Non-dominated Sorting Genetic Algorithm-II (NSGA-II). The accuracy of NSGA-II in this context is verified by considering a small-sized problem where it is easy to verify solutions. Furthermore, we consider a realistic problem instance for a battalion with 400 agents and 66 tasks in the initial schedule. We present the computational results of rescheduling when unpredictable activities emerge.
Uploads
Papers by Irfan Younas