An Expert System for Intelligent Selection of Proper Particle Swarm Optimization Variants Journal Article

Writer: Masehian، Ellips ؛ Eghbal Akhlaghi، Vahid ؛ Akbaripour، Hossein ؛ Sedighizadeh، Davoud ؛

Supply and Operations Management May 2015, Volume 2 - Number 1 (‎26 page(s) - From 569 to 594 )

Keywords: Particle Swarm Optimization Taxonomy PSO Variants Expert System Knowledge Base PSO Particle Swarm Optimization Algorithm Hybrid Particle Swarm Optimization Algorithm

Abstract:

Regarding the large number of developed Particle Swarm Optimization (PSO) algorithms and the various applications for which PSO has been used, selecting the most suitable variant of PSO for solving a particular optimization problem is a challenge for most researchers. In this paper, using a comprehensive survey and taxonomy on different types of PSO, an Expert System (ES) is designed to identify the most proper PSO for solving different optimization problems. Algorithms are classified according to aspects like particle, variable, process, and swarm. After integrating different acquirable information and forming the knowledge base of the ES consisting 100 rules, the system is able to logically evaluate all the algorithms and report the most appropriate PSObased approach based on interactions with users, referral to knowledge base and necessary inferences via user interface. In order to examine the validity and efficiency of the system, a comparison is made between the system outputs against the algorithms proposed by newly published articles. The result of this comparison showed that the proposed ES can be considered as a proper tool for finding an appropriate PSO variant that matches the application under consideration.

Machine summary:

Keywords: Particle Swarm Optimization; Taxonomy; PSO Variants; Expert System; Knowledge Base. In this paper, trying to lay out a powerful and intelligent method for finding and selecting the best type of PSO regarding the features of a given research problem, we have proposed using an Expert System (ES), which is by definition a computer program that simulates the thinking way of a specialist in a particular field. In the following, we introduce some well-known PSO-based algorithms as examples of developments the Basic PSO has undergone after its inception: Repulsive Particle Swarm Optimization (REPSO) (Lee et al. Taxonomy of the attributes of PSO-based methods Constrained/ Unconstrained Constrainment Deterministic / Stochastic Stochasticity Continuous/ Integer / Continuous + Integer Type Restricted / Unrestricted Velocity / Vertical Velocity / Limited Velocity / Escape Velocity / Adaptive Velocity Velocity Type Variables Fuzzy / Crisp Fuzziness Continuous/ Discrete / Binary Space Continuity Adaptive/ Dissipative / Adaptive + Dissipative Accordance Attractive/ Repulsive / Attractive + Repulsive Attraction Aggregation / Passive / Active / Congregation / Passive/ Social Association Newtonian/ Quantum Dynamics Particles Hierarchical/ Non-hierarchical Hierarchy Static/ Dynamic Mobility Synchronous/ Asynchronous Synchronicity Positive/ Negative Trajectory Cooperative/ Un-Cooperative Cooperation Gbest/ Lbest/ Pyramid/ Star/ Small World/ Von-Neumann / Random Graphs/ Topology Swarm Active/ Passive Activity Divided / Undivided Divisibility Recursive/ Sideway Recursiveness Genetic Algorithms/ Ant Colony Optimization / Differential Evolution / Immune Systems/ Neural Networks Hybridization Process Single/ Multiple Objective Interactive/ Non-Interactive User Interaction 2.

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