Abstract
Despite of its simplicity, the conventional learning strategy of canonical particle swarm optimization (PSO) is inefficient to handle complex optimization problems due to its tendency of overemphasizing the fitness information of global best position without considering the diversity information of swarm. In this article, a modified particle swarm optimization with effective guides (MPSOEG) is proposed, aiming to improve the algorithm's search performances in handling the optimization problems with different characteristics. Depending on the search performance of algorithm, two types of exemplars can be generated by an optimal guide creation (OGC) module incorporated into MPSOEG by referring to the particles with valuable directional information. Particularly, a global exemplar is generated by OCG module to guide the swarm converging towards the promising solution regions of search space, whereas a unique local exemplar can be customized for each particle to enable it escaping from local or non-optimal solution regions. In contrary to global best particle, the exemplars generated by OGC module are able to guide all MPSOEG particles more effectively by considering both fitness and diversity information of swarm, hence can achieve better balancing of algorithm's exploration and exploitation searches. Another notable contribution of MPSOEG is the simplicity of its learning framework through the elimination of both inertia weight and acceleration coefficients parameters. Comprehensive simulation studies are conducted with 25 benchmark functions and the proposed MPSOEG is reported to outperform its six peer algorithms in terms of search accuracy, search reliability and search efficiency in most tested problems.
Highlights
The rapid technological advancement in Industry Revolution 4.0 has led to the deployment of various engineering systems that can be described as the complicated optimization models with non-differentiable, non-linear, discontinuous and multimodal characteristics
PARAMETER SETTINGS OF ALL particle swarm optimization (PSO) VARIANTS The proposed modified particle swarm optimization with effective guides (MPSOEG) is compared with six well established PSO variants, i.e., the original PSO commonly known as basic PSO (BPSO) [33], eXpanded PSO (XPSO) [58], two-swarm learning PSO (TSLPSO) [68], fitness-based multi-role PSO (FMPSO) [69], dynamic tournament topology PSO (DTTPSO) [71] and grouping PSO (GPSO) [72]
In this article, a new PSO variant known as MPSOEG is proposed to solve unconstrained single objective optimization problems more effectively and efficiently
Summary
The rapid technological advancement in Industry Revolution 4.0 has led to the deployment of various engineering systems that can be described as the complicated optimization models with non-differentiable, non-linear, discontinuous and multimodal characteristics. Are not able to solve these challenging optimization problems effectively due to their drawbacks of limited global search, strong dependency on gradient information and poor guessing of initial solutions [3]. Metaheuristic search algorithms emerge as promising candidate optimizers to solve these challenging modern engineering optimization problems by leveraging. As compared with traditional optimization approaches, metaheuristic search algorithms have more competitive advantages such as good global search ability, not requiring gradient information and accurate guessing of initial solutions in order to obtain the optimal or near optimal solution of optimization problem. Evolutionary algorithms are inspired by Darwinian theory of evolution where better offspring solutions are produced using different genetic operators known as recombination, survival of fittest and mutation.
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