Adaptive Particle Swarm Optimization with Heterogeneous Multicore Parallelism and GPU Acceleration

Wachowiak, Mark P.; Timson, Mitchell C.; DuVal, David J.

doi:10.1109/tpds.2017.2687461

Cited by 31 publications

(11 citation statements)

References 44 publications

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“…Therefore, the parameter b is introduced into the new formula to indicate the frequency of the new solution is inferior to the old solution. In this way, assuming that the kth block chicken swarm is the result of kth iteration of the previous generation, then the relation between the Equation (12) and block k is established and obtains a new equation as follows:…”

Section: Parallel Iterative Strategymentioning

confidence: 99%

“…for harmony search on CUDA to solve traveling salesman problem (TSP), 9 parallel PSO on GPU, [10][11][12] a parallel bees colony algorithm implementation on GPU, 13 parallel genetic algorithm, 14,15 parallel local search algorithm. 16,17 These GPU-based implementations of intelligent optimization algorithms have indicated that the GPU can be applied to significantly improve the performance of the algorithms.…”

mentioning

confidence: 99%

“…The experiment results demonstrate that the proposed method significantly increases the computational speed and improves the accuracy of solutions by using a larger swarm size. Wachowiak et al12 presented…”

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confidence: 99%

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Graphic process units‐based chicken swarm optimization algorithm for function optimization problems

Lin

Zhong

Chen

2020

Concurrency and Computation

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This article focuses on how to design an efficient GPU-based chicken swarm optimization (CSO) algorithm (GCSO), so as to improve diversity and speed up convergence by running a large number of populations in parallel. GCSO mainly improves the sequential CSO in three aspects: (i) GCSO modifies the location updating equation of the rooster and proposes a parallel iterative strategy to transform the sequential iteration process into a parallel iterative process, thereby achieving fine-grained parallelism and improving the convergence speed. (ii) A multirange search strategy is proposed to build different neighborhoods for each flock on the graphic process units (GPU), so that each flock searched in their respective neighborhoods, thus increasing the density and diversity of the search, and making it not easy to fall into a local optimum. (iii) A new column storage structure is designed to meet the requirement of coalescent access on GPU. Twelve benchmark functions are selected to compare GCSO algorithm with some sequential intelligence optimization algorithms and the GPU-based particle swarm algorithm. The results show that the GCSO is able to obtain a speedup up to 163.09× compared with the CSO and achieve better optimization results in terms of both optimization accuracy and convergence speed than some intelligence optimization algorithms.

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Section: Parallel Iterative Strategymentioning

confidence: 99%

mentioning

confidence: 99%

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Graphic process units‐based chicken swarm optimization algorithm for function optimization problems

Lin

Zhong

Chen

2020

Concurrency and Computation

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“…-Multiple strategies-based orthogonal design particle swarm optimizer (MSODPSO) [64], -Particle swarm optimization algorithm with parasitic behavior (PSOPB) [62], -Particle swarm optimization with dynamical exploitation space reduction strategy (DESP-PSO) [21], -Particle swarm optimization with an aging leader and challengers (ALC-PSO) [13], -Adaptive particle swarm optimization with heterogeneous multicore parallelism and GPU acceleration [85]. 4.…”

Section: The Second Aims To Enhance the Population Diversity Bymentioning

confidence: 99%

A quarter century of particle swarm optimization

Cheng

Lei

et al. 2018

Complex Intell. Syst.

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Particle swarm optimization (PSO) is a population-based stochastic algorithm modeled on the social behaviors observed in flocking birds. Over the past quarter century, the particle swarm optimization algorithm has attracted many researchers' attention. Through the convergent operation and divergent operation, individuals in PSO group and diverge in the search space/objective space. In this paper, the historical development, the state-of-the-art, and the applications of the PSO algorithms are reviewed. In addition, the characteristics and issues of the PSO algorithm are also discussed from the evolution and learning perspectives. Every individual in the PSO algorithm learns from itself and another particle with good fitness value. The search performance and convergence speed were affected by different learning strategies. The scheduling and data-mining problems are illustrated as two typical cases of PSO algorithm solving real-world application problems. With the analysis of different evolution and learning strategies, particle swarm optimization algorithm could be utilized on solving more real-world application problems effectively, and the strength and limitation of various PSO algorithms could be revealed.

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“…Some recent algorithms include, for example, a sine-cosine algorithm [27], a grey wolf optimiser [20], teaching-learning-based optimisation [2], and Jaya algorithm [28]. Meanwhile, powerful existing algorithms such as PSO and DE have been upgraded by 2 Mathematical Problems in Engineering integrating into them some types of self-adaptive schemes, for example, adaptive differential evolution with optional external archive (JADE) [29], Success-History Based Parameter Adaptation for Differential Evolution (SHADE) [30], SHADE Using Linear Population Size Reduction (LSHADE) [31], and adaptive PSO [32][33][34]. MHs are even more popular when they can be used to find a Pareto front of a multiobjective optimisation problem within one optimisation run.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Distribution Algorithm Using Correlation between Binary Elements: A New Binary-Code Metaheuristic

Pholdee

Bureerat

2017

Mathematical Problems in Engineering

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A new metaheuristic called estimation of distribution algorithm using correlation between binary elements (EDACE) is proposed. The method searches for optima using a binary string to represent a design solution. A matrix for correlation between binary elements of a design solution is used to represent a binary population. Optimisation search is achieved by iteratively updating such a matrix. The performance assessment is conducted by comparing the new algorithm with existing binary-code metaheuristics including a genetic algorithm, a univariate marginal distribution algorithm, population-based incremental learning, binary particle swarm optimisation, and binary simulated annealing by using the test problems of CEC2015 competition and one real-world application which is an optimal flight control problem. The comparative results show that the new algorithm is competitive with other established binary-code metaheuristics.

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Adaptive Particle Swarm Optimization with Heterogeneous Multicore Parallelism and GPU Acceleration

Cited by 31 publications

References 44 publications

Graphic process units‐based chicken swarm optimization algorithm for function optimization problems

Graphic process units‐based chicken swarm optimization algorithm for function optimization problems

A quarter century of particle swarm optimization

Estimation of Distribution Algorithm Using Correlation between Binary Elements: A New Binary-Code Metaheuristic

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