A novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization

Yazdani, Danial; Nasiri, Babak; Sepas-Moghaddam, Alireza; Meybodi, Mohammad Reza

doi:10.1016/j.asoc.2012.12.020

Cited by 116 publications

(81 citation statements)

References 16 publications

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“…Another similar idea 105 is the hibernation multi-swarm optimization (HmSO) algorithm, introduced in [30], where a child swarm hibernates if it is not productive anymore and wakes up when a change is detected. Recently, a similar hibernation scheme was employed in [70,69], in which competitive results were reported in comparison with a set of algorithms.…”

Section: Variable Number Of Populationsmentioning

confidence: 99%

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Multi-population methods in unconstrained continuous dynamic environments: The challenges

Nguyễn

Yang

et al. 2015

Information Sciences

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The multi-population method has been widely used to solve unconstrained continuous dynamic optimization problems with the aim of maintaining multiple populations on different peaks to locate and track multiple changing peaks simultaneously. However, to make this approach efficient, several crucial challenging issues need to be addressed, e.g., how to determine the moment to react to changes, how to adapt the number of populations to changing environments, and how to determine the search area of each population. In addition, several other issues, e.g., communication between populations, overlapping search, the way to create multiple populations, detection of changes, and local search operators, should be also addressed. The lack of attention on these challenging issues within multi-population methods hinders the development of multi-population based algorithms in dynamic environments. In this paper, these challenging issues are comprehensively analyzed by a set of experimental studies from the algorithm design point of view. Experimental studies based on a set of popular algorithms show that the performance of algorithms is significantly affected by these challenging issues on the moving peaks benchmark.

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Section: Variable Number Of Populationsmentioning

confidence: 99%

“…The representative model for the second way of organizing populations is a model called collaborative evolutionary 70 swarm optimization (CESO), proposed in [43]. In CESO, two swarms, which use the crowding DE (CDE) [60] and PSO model, respectively, cooperate with each other by a collaborative mechanism.…”

Section: Introductionmentioning

confidence: 99%

Multi-population methods in unconstrained continuous dynamic environments: The challenges

Nguyễn

Yang

et al. 2015

Information Sciences

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“…In addition, particles and swarms can maintain a reasonable distance by the definition of correlation coefficient in Equations (5) and (9). A reasonable distance can be obtained by a correlation analysis in advance.…”

Section: Adaptive Cooperative Mechanism Of Multi-swarm Particlesmentioning

confidence: 99%

“…Nevertheless, similar to most stochastic algorithms, the performance of PSO algorithm will suffer from a significant degradation when it is applied to multi-dimensional or tightly coupled problems. To improve the performance of the traditional PSO algorithm and overcome the disadvantages of traditional PSO algorithm, different variations are presented and studied, such as hybrid PSO algorithm [7,8], multi-swarm PSO algorithm [9,10] and Quantum-behaved PSO algorithm [11].…”

Section: Introductionmentioning

confidence: 99%

An Orthogonal Multi-Swarm Cooperative PSO Algorithm with a Particle Trajectory Knowledge Base

2017

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Abstract:A novel orthogonal multi-swarm cooperative particle swarm optimization (PSO) algorithm with a particle trajectory knowledge base is presented in this paper. Different from the traditional PSO algorithms and other variants of PSO, the proposed orthogonal multi-swarm cooperative PSO algorithm not only introduces an orthogonal initialization mechanism and a particle trajectory knowledge base for multi-dimensional optimization problems, but also conceives a new adaptive cooperation mechanism to accomplish the information interaction among swarms and particles. Experiments are conducted on a set of benchmark functions, and the results show its better performance compared with traditional PSO algorithm in aspects of convergence, computational efficiency and avoiding premature convergence.

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“…Due to the low cost and the popular GPUinside desktops and laptops, more and more researchers focus on how to parallelize various algorithms on GPU architecture. On the other hand, computational intelligence has been successfully applied to solve many kinds of applications [6][7][8][9]. Researchers have investigated how to use GPU computing to accelerate computational intelligence.…”

Section: Introductionmentioning

confidence: 99%

Avoiding Duplicated Computation to Improve The Performance of PFSP on CUDA GPUs

Wu¹,

Wei²,

Lai³

et al. 2016

Computer Science &Amp; Information Technology ( CS &Amp; IT )

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Graphics Processing Units (GPUs) have been emerged as powerful parallel compute platforms for various application domains. A GPU consists of hundreds or even thousands processor cores and adopts Single Instruction Multiple Threading (SIMT) architecture. Previously, we have proposed an approach that optimizes the Tabu Search algorithm for solving the Permutation Flowshop Scheduling Problem (PFSP) on a GPU by using a math function to generate all different permutations, avoiding the need of placing all the permutations in the global memory. Based on the research result, this paper proposes another approach that further improves the performance by avoiding duplicated computation among threads

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A novel multi-swarm algorithm for optimization in dynamic environments based on particle swarm optimization

Cited by 116 publications

References 16 publications

Multi-population methods in unconstrained continuous dynamic environments: The challenges

Multi-population methods in unconstrained continuous dynamic environments: The challenges

An Orthogonal Multi-Swarm Cooperative PSO Algorithm with a Particle Trajectory Knowledge Base

Avoiding Duplicated Computation to Improve The Performance of PFSP on CUDA GPUs

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