Particle Swarm Optimization algorithm with multiple social learning structures

Pongchairerks, Pisut; Kachitvichyanukul, Voratas

doi:10.1504/ijor.2009.026534

Cited by 47 publications

(28 citation statements)

References 14 publications

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“…However, no research literature is found that addresses this issue. Pongchairerks and Kachitvichyanukul (2005) proposed a use of heterogeneous population in PSO to allow some fraction of the swarm to move by crossover with the best particle.…”

Section: The Effect Of Sub-groupingmentioning

confidence: 99%

Comparison of Three Evolutionary Algorithms: GA, PSO, and DE

Kachitvichyanukul¹

2012

Industrial Engineering and Management Systems

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This paper focuses on three very similar evolutionary algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). While GA is more suitable for discrete optimization, PSO and DE are more natural for continuous optimization. The paper first gives a brief introduction to the three EA techniques to highlight the common computational procedures. The general observations on the similarities and differences among the three algorithms based on computational steps are discussed, contrasting the basic performances of algorithms. Summary of relevant literatures is given on job shop, flexible job shop, vehicle routing, location-allocation, and multimode resource constrained project scheduling problems.

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Section: The Effect Of Sub-groupingmentioning

confidence: 99%

Comparison of Three Evolutionary Algorithms: GA, PSO, and DE

Kachitvichyanukul¹

2012

Industrial Engineering and Management Systems

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219

122

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“…Several approaches have been proposed to deal with this problem. One of the approaches, introduced by Pongchairerks and Kachitvichyanukul (2009b), utilizes multiple social learning terms and extends the concept of the standard PSO in GLNPSO. Instead of using the global best particle only as reference, GLNPSO also incorporates the local best and near-neighbor best (Veeramachaneni et al, 2003) as additional social learning reference terms.…”

Section: Glnpsomentioning

confidence: 99%

An Efficient PSO Algorithm for Finding Pareto-Frontier in Multi-Objective Job Shop Scheduling Problems

Wisittipanich¹,

Kachitvichyanukul²

2013

Industrial Engineering and Management Systems

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In the past decades, several algorithms based on evolutionary approaches have been proposed for solving job shop scheduling problems (JSP), which is well-known as one of the most difficult combinatorial optimization problems. Most of them have concentrated on finding optimal solutions of a single objective, i.e., makespan, or total weighted tardiness. However, real-world scheduling problems generally involve multiple objectives which must be considered simultaneously. This paper proposes an efficient particle swarm optimization based approach to find a Pareto front for multi-objective JSP. The objective is to simultaneously minimize makespan and total tardiness of jobs. The proposed algorithm employs an Elite group to store the updated non-dominated solutions found by the whole swarm and utilizes those solutions as the guidance for particle movement. A single swarm with a mixture of four groups of particles with different movement strategies is adopted to search for Pareto solutions. The performance of the proposed method is evaluated on a set of benchmark problems and compared with the results from the existing algorithms. The experimental results demonstrate that the proposed algorithm is capable of providing a set of diverse and high-quality nondominated solutions.

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“…The GLN-PSO algorithm is an extension of the standard PSO algorithm using the global, local, and near neighbour best positions simultaneously in order to update the particle velocities 10,11 . It outperforms the standard version in terms of solution quality.…”

Section: Description Of Gln-pso C Algorithmmentioning

confidence: 99%

“…There are many variants of the PSO algorithm in the literature [9][10][11][12] . We will use the GLN-PSO c algorithm 10,11 since it enables the swarm to explore different parts of the search spaces simultaneously. This algorithm is an extension of the GLN-PSO algorithm, some solutions of which are generated via the crossover operator.…”

Section: Introductionmentioning

confidence: 99%

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Pongchairerks¹

2009

ScienceAsia

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This research introduces three heuristic algorithms for solving job-shop scheduling problems, job-shop scheduling problems with multi-purpose machines, and open-shop scheduling problems. All these algorithms are based on the particle swarm optimization algorithm, and generate solutions that belong to the class of parameterized active schedules via their specific decoding procedures. Comparison of the benchmark test results of the proposed algorithms with those of existent algorithms reveal that the proposed algorithms perform better in some instances.

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Particle Swarm Optimization algorithm with multiple social learning structures

Cited by 47 publications

References 14 publications

Comparison of Three Evolutionary Algorithms: GA, PSO, and DE

Comparison of Three Evolutionary Algorithms: GA, PSO, and DE

An Efficient PSO Algorithm for Finding Pareto-Frontier in Multi-Objective Job Shop Scheduling Problems

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