Talks by Mohammad Majid al-Rifaie
Papers by Mohammad Majid al-Rifaie
Stochastic Diffusion Search, first incepted in 1989, belongs to the extended family of swarm inte... more Stochastic Diffusion Search, first incepted in 1989, belongs to the extended family of swarm intelligence algorithms. In contrast to many nature-inspired algorithms, stochastic diffusion search has a strong mathematical framework describing its behaviour and convergence. In addition to concisely exploring the algorithm in the context of natural swarm intelligence systems, this paper reviews various developments of the algorithm, which have been shown to perform well in a variety of application domains including continuous optimisation, implementation on hardware and medical imaging. This algorithm has also being utilised to argue the potential computational creativity of swarm intelligence systems through the two phases of exploration and exploitation.
In recent years studies of social agents have suggested several new metaheuristics for use in sea... more In recent years studies of social agents have suggested several new metaheuristics for use in search and optimisation; Stochastic Diffusion Search (SDS)[1] is one such 'Swarm Intelligence'algorithm. SDS is a distributed population based search algorithm utilising interaction between simple agents to locate a global optimum; such 'communicating agents' have recently been suggested as a potential metaphor for some cognitive processes [6].
The integration of Swarm Intelligence (SI) algorithms and Evolutionary algorithms (EAs) might be ... more The integration of Swarm Intelligence (SI) algorithms and Evolutionary algorithms (EAs) might be one of the future approaches in the Evolutionary Computation (EC). This work narrates the early research on using Stochastic Diffusion Search (SDS)--a swarm intelligence algorithm--to empower the Differential Evolution (DE)--an evolutionary algorithm--over a set of optimisation problems.
A novel approach of integrating two swarm intelligence algorithms is considered, one simulating t... more A novel approach of integrating two swarm intelligence algorithms is considered, one simulating the behaviour of birds flocking (Particle Swarm Optimisation) and the other one (Stochastic Diffusion Search) mimics the recruitment behaviour of one species of ants���Leptothorax acervorum. This hybrid algorithm is assisted by a biological mechanism inspired by the behaviour of blood flow and cells in blood vessels, where the concept of high and low blood pressure is utilised.
Abstract���This paper introduces a novel approach in using a swarm intelligence algorithm���Stoch... more Abstract���This paper introduces a novel approach in using a swarm intelligence algorithm���Stochastic Diffusion Search���as a tool to identify metastasis in bone scans and microcalcifications on the mammographs. This algorithm is adapted for this particular purpose and its performance is investigated by running the agents of the swarm intelligence algorithm on sample bone scans whose status have been determined by the experts.
Abstract This work introduces two swarm intelligence algorithms���one mimicking the behaviour of ... more Abstract This work introduces two swarm intelligence algorithms���one mimicking the behaviour of one species of ants (Leptothorax acervorum) foraging (a 'stochastic diffusion search', SDS) and the other algorithm mimicking the behaviour of birds flocking (a 'particle swarm optimiser', PSO)���and outlines a novel integration strategy exploiting the local search properties of the PSO with global SDS behaviour.
This study reports early research aimed at applying the powerful resource allocation mechanism de... more This study reports early research aimed at applying the powerful resource allocation mechanism deployed in Stochastic Diffusion Search (SDS) to the Particle Swarm Optimiser (PSO) metaheuristic, effectively merging the two swarm intelligence algorithms. The results reported herein suggest that the hybrid algorithm, exploiting information sharing between particles, has the potential to improve the optimisation capability of conventional PSOs.
This work details early research aimed at applying the powerful resource allocation mechanism dep... more This work details early research aimed at applying the powerful resource allocation mechanism deployed in Stochastic Diffusion Search (SDS) to the Differential Evolution (DE), effectively merging a nature inspired swarm intelligence algorithm with a biologically inspired evolutionary algorithm. The results reported herein suggest that the hybrid algorithm, exploiting information sharing between the population, has the potential to improve the optimisation capability of classical DE.
Bare Bones PSO was proposed by Kennedy as a model of PSO dynamics. Dependence on velocity is repl... more Bare Bones PSO was proposed by Kennedy as a model of PSO dynamics. Dependence on velocity is replaced by sampling from a Gaussian distribution. Although Kennedy's original formulation is not competitive to standard PSO, the addition of a component-wise jumping mechanism, and a tuning of the standard deviation, can produce a comparable optimisation algorithm. This algorithm, Bare Bones with Jumps, exists in a variety of formulations.
I recognise that the copyright and other relevant Intellectual Property Rights (IPR) of the above... more I recognise that the copyright and other relevant Intellectual Property Rights (IPR) of the abovedescribed thesis rests with the author and/or other IPR holders and that no quotation from it or information derived from it may be published without the prior written consent of the author.
Abstract. In the spirit of Searle's definition of weak and strong artificial intelligence, this p... more Abstract. In the spirit of Searle's definition of weak and strong artificial intelligence, this paper presents a discussion on weak computational creativity in swarm intelligence systems. It addresses the concepts of freedom and constraint and their impact on the creativity of the underlying systems.
In this work, a novel approach of merging two swarm intelligence algorithms is considered���one m... more In this work, a novel approach of merging two swarm intelligence algorithms is considered���one mimicking the behaviour of ants foraging (Stochastic Diffusion Search [5]) and the other algorithm simulating the behaviour of birds flocking (Particle Swarm Optimisation [17]). This hybrid algorithm is assisted by a mechanism inspired from the behaviour of skeletal muscles activated by motor neurons. The operation of the swarm intelligence algorithms is first introduced via metaphor before the new hybrid algorithm is defined.
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Talks by Mohammad Majid al-Rifaie
Papers by Mohammad Majid al-Rifaie