A dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution

Zheng, Jinhua; Zhang, Zeyu; Zou, Juan; Yang, Shengxiang; Ou, Junwei; Hu, Yaru

doi:10.1016/j.swevo.2021.100987

Cited by 30 publications

(8 citation statements)

References 58 publications

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“…Considering adversarial decomposition and neighborhood evolution using the complementary characteristics in the search area, the authors of [20] proposed a dynamic multi-objective PSO approach. In [21], the authors proposed a neighborhood-based PSO with discrete crossover using a two-stage framework to balance exploitation and exploration.…”

Section: Related Workmentioning

confidence: 99%

Heterogeneous Parallel Implementation of PSO Algorithm with an Aging Leader and Challengers

Yazdanpanah

2022

Preprint

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Parallel computing techniques provide high-performance execution of large-size and real-world complicated problems such as heuristic optimization algorithms. One of the most popular optimization algorithms is PSO (particle swarm optimization) which is an intelligent search method for finding the best solution according to population state. The PSO algorithm has obtained significant attention from researchers and different parallel implementations of this algorithm have been presented for intensive-computing applications. The ALC-PSO algorithm (PSO with an aging leader and challengers) is an improved population-based and intensive-computation procedure thanks to the high fitness analyses. Compared to the traditional PSO, the ALC-PSO algorithm improves convergence rapidity. In this paper, we have developed a novel heterogeneous parallel implementation of the ALC-PSO algorithm using OmpSs and CUDA, for execution on both CPU and GPU cores. OmpSs is a task-based parallel programming model that uses runtime system and hardware for automatic exploiting and managing parallelisms. Combination of OmpSs with CUDA helps developers accelerate their applications running on both CPUs and GPUs. The results demonstrate that the proposed approach (i.e., the OmpSs-CUDA implementation of the ALC-PSO algorithm) provides higher performance than the serial, and also the CUDA-based parallel implementations of the ALC-PSO algorithm.

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Section: Related Workmentioning

confidence: 99%

Heterogeneous Parallel Implementation of PSO Algorithm with an Aging Leader and Challengers

Yazdanpanah

2022

Preprint

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“…To gauge the potency of this proposed method, we employ distinct benchmark test functions: ZDT 65 , DTLZ 66 , Constraint 67 , 68 (CONSTR, TNK, SRN, BNH, OSY and KITA) and real-world engineering design Brushless DC wheel motor 69 (RWMOP1), Helical spring 68 (RWMOP2), Two-bar truss 68 (RWMOP3), Welded beam 70 (RWMOP4), Disk brake 71 (RWMOP5). The objective of this assessment is to compare the efficacy of our proposed method against MOMPA, NSGA-II, MOAOA, MOEA/D and MOGNDO, using metrics like generational distance (GD) 34 , inverse generational distance (IGD) 35 , hypervolume 36 , Spacing 37 , Spread 36 and run time (RT). The approximations of the Pareto-front produced by our method are evaluated using these metrics.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective exponential distribution optimizer (MOEDO): a novel math-inspired multi-objective algorithm for global optimization and real-world engineering design problems

Kalita,

Ramesh,

Cepova

et al. 2024

Sci Rep

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The exponential distribution optimizer (EDO) represents a heuristic approach, capitalizing on exponential distribution theory to identify global solutions for complex optimization challenges. This study extends the EDO's applicability by introducing its multi-objective version, the multi-objective EDO (MOEDO), enhanced with elite non-dominated sorting and crowding distance mechanisms. An information feedback mechanism (IFM) is integrated into MOEDO, aiming to balance exploration and exploitation, thus improving convergence and mitigating the stagnation in local optima, a notable limitation in traditional approaches. Our research demonstrates MOEDO's superiority over renowned algorithms such as MOMPA, NSGA-II, MOAOA, MOEA/D and MOGNDO. This is evident in 72.58% of test scenarios, utilizing performance metrics like GD, IGD, HV, SP, SD and RT across benchmark test collections (DTLZ, ZDT and various constraint problems) and five real-world engineering design challenges. The Wilcoxon Rank Sum Test (WRST) further confirms MOEDO as a competitive multi-objective optimization algorithm, particularly in scenarios where existing methods struggle with balancing diversity and convergence efficiency. MOEDO's robust performance, even in complex real-world applications, underscores its potential as an innovative solution in the optimization domain. The MOEDO source code is available at: https://github.com/kanak02/MOEDO.

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“…We aimed to gauge their capabilities in swiftly converging to the true Pareto optimal front and the distribution of the obtained non-dominated solutions. Upon assessing their convergence and coverage using MO metrics and the produced Pareto optimal fronts on benchmark suites (ZDT [ 54 ], DTLZ [ 54 ], Constraint [ 68 , 69 ] and engineering design problems [ 55 , 56 ]), we discerned that these algorithms still exhibited shortcomings in convergence and coverage using metrics like generational distance (GD) [ 70 ], inverse generational distance (IGD) [ 71 ], hypervolume [ 72 ], Spacing [ 73 ], Spread [ 72 ] and run time (RT). The approximations of the Pareto-front produced by our method are evaluated using these metrics.…”

Section: Introductionmentioning

confidence: 99%

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

Kalita,

Naga Ramesh,

Čep

et al. 2024

Heliyon

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A dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution

Cited by 30 publications

References 58 publications

Heterogeneous Parallel Implementation of PSO Algorithm with an Aging Leader and Challengers

Heterogeneous Parallel Implementation of PSO Algorithm with an Aging Leader and Challengers

Multi-objective exponential distribution optimizer (MOEDO): a novel math-inspired multi-objective algorithm for global optimization and real-world engineering design problems

Multi-objective liver cancer algorithm: A novel algorithm for solving engineering design problems

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