Best-Worst-Play (BWP): A metaphor-less Optimization Algorithm

Singh, Ramanpreet; Kumar, G. Pradeep; Pathak, Vimal Kumar; Singh, Prem; Chaudhary, Himanshu

doi:10.1088/1742-6596/1455/1/012007

Cited by 10 publications

(6 citation statements)

References 8 publications

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“…The Best-Worst-Play (BWP) algorithm [14] is a metaphor-less optimization approach that combines elements from two algorithms, Jaya [10] and Rao-1 [13]. The algorithm aims to strike a balance between exploration and exploitation in solving unconstrained and constrained optimization problems by employing two operators inspired by Jaya and Rao-1 that are executed consecutively.…”

Section: Best-worst-play Algorithmmentioning

confidence: 99%

GPU-Based Acceleration of Metaphor-less Algorithms and Their Application for Solving Large Systems of Nonlinear Equations

Silva,

Lopes

2024

Preprint

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Traditional population-based metaheuristic algorithms are effective in solving complex real-world problems but require careful strategy selection and parameter tuning. Parameter-less population-based optimization algorithms have gained importance due to their simplicity and efficiency. However, research on their applicability for solving large systems of nonlinear equations is still incipient. In this paper, a systematic review and detailed description of metaphor-less optimization algorithms is made, including the Jaya algorithm, the main Jaya variants for continuous optimization problems, with a focus on the Enhanced Jaya (EJAYA) algorithm, the three Rao algorithms, the best-worst-play (BWP) algorithm, and the very recent max-min-greedy-interaction (MaGI) algorithm. This paper also discusses a few previous parallelizations of the Jaya optimization algorithm, as well as recent GPU-based massively parallel implementations of the Jaya, Enhanced Jaya (EJAYA), Rao, and BWP algorithms developed by the authors. In addition, a novel GPU-accelerated version of the MaGI algorithm is proposed. The GPU-accelerated versions of the metaphor-less algorithms developed by the authors were implemented using the Julia programming language and tested primarily on GeForce RTX 3090 and NVIDIA A100 GPUs, as well as on Tesla V100S and Tesla T4 GPUs, using a set of difficult, large-scale nonlinear equation system problems. The computational experiments carried out produced quite significant speedups, which highlights the efficiency of the GPU-based versions of the metaphor-less algorithms developed for solving large-scale systems of nonlinear equations.

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Section: Best-worst-play Algorithmmentioning

confidence: 99%

GPU-Based Acceleration of Metaphor-less Algorithms and Their Application for Solving Large Systems of Nonlinear Equations

Silva,

Lopes

2024

Preprint

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“…The theoretical underpinnings and practical considerations of Jaya [12], EJAYA [15], Rao [17], BWP [18], and MaGI [14] algorithms are presented here. This section provides a comprehensive exploration of these methods, as well as detailed instructions and guidelines for implementing the algorithms.…”

Section: Metaphorless Optimization Algorithmsmentioning

confidence: 99%

“…This paper provides a review and detailed description of metaphorless algorithms, including the Jaya optimization algorithm [12], the enhanced Jaya (EJAYA) algorithm [15], which is a variant of Jaya that has recently been shown to be effective in solving nonlinear equation systems [16], the three Rao algorithms [17], the best-worst-play (BWP) algo-rithm [18], and the recent max-min greedy interaction (MaGI) algorithm [14]. In addition to examining prior parallelizations of the Jaya optimization algorithm, this paper introduces enhanced GPU-based massively parallel implementations of these algorithms, employing a more efficient parallelization strategy.…”

Section: Introductionmentioning

confidence: 99%

Parallel GPU-Acceleration of Metaphorless Optimization Algorithms: Application for Solving Large-Scale Nonlinear Equation Systems

Silva,

Lopes,

Mendonça

2024

Applied Sciences

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Traditional population-based metaheuristic algorithms are effective in solving complex real-world problems but require careful strategy selection and parameter tuning. Metaphorless population-based optimization algorithms have gained importance due to their simplicity and efficiency. However, research on their applicability for solving large systems of nonlinear equations is still incipient. This paper presents a review and detailed description of the main metaphorless optimization algorithms, including the Jaya and enhanced Jaya (EJAYA) algorithms, the three Rao algorithms, the best-worst-play (BWP) algorithm, and the new max–min greedy interaction (MaGI) algorithm. This article presents improved GPU-based massively parallel versions of these algorithms using a more efficient parallelization strategy. In particular, a novel GPU-accelerated implementation of the MaGI algorithm is proposed. The GPU-accelerated versions of the metaphorless algorithms developed were implemented using the Julia programming language. Both high-end professional-grade GPUs and a powerful consumer-oriented GPU were used for testing, along with a set of hard, large-scale nonlinear equation system problems to gauge the speedup gains from the parallelizations. The computational experiments produced substantial speedup gains, ranging from 33.9× to 561.8×, depending on the test parameters and the GPU used for testing. This highlights the efficiency of the proposed GPU-accelerated versions of the metaphorless algorithms considered.

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“…If the iteration times t is smaller than F t , update the particle velocity and position by equations ( 13)- (15). Otherwise, update the particle by equations ( 18)- (20).…”

Section: Mppso Algorithm Flowmentioning

confidence: 99%

“…Vimal et al are devoted to the improvement and application of different novel algorithms and have conducted many meaningful researches and conclusions. [17][18][19][20] A teaching-learning-based optimization and PSO algorithm is proposed merging teaching-learning-based optimization (TLBO) and PSO algorithms, and the feasibility in determining the optimal parameters of scanning process is proved. 21 Moreover, the PSO algorithm is modified to improve the search ability and convergence performance by employing the greedy selection procedure.…”

Section: Introductionmentioning

confidence: 99%

Optimal allocation of product reliability using novel multi-population particle swarm optimization algorithm

Zhang

Ran

Zhang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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Reliability optimal allocation is a critical method in engineering and operation fields to improve and ensure product quality. However, conventional model generally uses the single objective with research and development (R & D) cost or product reliability. Moreover, the popular particle swarm optimization (PSO) algorithm solving the model has insufficient global search ability. In this paper, a novel reliability optimal allocation method is developed based on double-objective model and multi-population PSO (MPPSO) algorithm. The cost function is established considering all the cost related to reliability using analytical hierarchical process (AHP) and ordered weighted averaging (OWA) operator. Multi-population particle swarm optimization algorithm is developed based on information exchange mechanism among subpopulations. In the algorithm, the inertia weight is optimized by chaotic map, and different mutation operations are carried out on the subpopulations. An illustrative example, which implements to computer numerical control (CNC) grinding machine, is presented to illustrate the practicality and application of the proposed method. Finally, the comparison and analysis are implemented to show the advantage of proposed model and MPPSO algorithm.

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Best-Worst-Play (BWP): A metaphor-less Optimization Algorithm

Cited by 10 publications

References 8 publications

GPU-Based Acceleration of Metaphor-less Algorithms and Their Application for Solving Large Systems of Nonlinear Equations

GPU-Based Acceleration of Metaphor-less Algorithms and Their Application for Solving Large Systems of Nonlinear Equations

Parallel GPU-Acceleration of Metaphorless Optimization Algorithms: Application for Solving Large-Scale Nonlinear Equation Systems

Optimal allocation of product reliability using novel multi-population particle swarm optimization algorithm

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