A New Hybrid Yin-Yang-Pair-Particle Swarm Optimization Algorithm for Uncapacitated Warehouse Location Problems

Heidari, Ali Asghar; Kazemizade, Omid; Hakimpour, Farshad

doi:10.5194/isprs-archives-xlii-4-w4-373-2017

Cited by 5 publications

(2 citation statements)

References 31 publications

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“…Later in 2017, the dynamical archive was incorporated in YYPO to formulate dynamical YYPO (dYYPO), where its performance has a great improvement [16] compared with YYPO. Also, Heidari et al combined the idea of PSO and YYPO to develop PSOYPO optimizer, which gives outstanding performance in solving the uncapacitated warehouse location problem [17]. YYPO has shown a wide application capability, e.g., to play an important role in a proposed control scheme in solar energy harvesting [2].…”

Section: Introductionmentioning

confidence: 99%

Meta-Heuristic Optimizer Inspired by the Philosophy of Yi Jing

Tang

Cai

Goh

2022

Preprint

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Drawing inspiration from the philosophy of Yi Jing, the Yin-Yang pair optimization (YYPO) algorithm has been shown to achieve competitive performance in single objective optimizations, in addition to the advantage of low time complexity when compared to other population-based meta-heuristics. Building upon Yi Jing, we propose the novel Yi optimization (YI) algorithm. Specifically, we enhance the Yin-Yang pair in YYPO with a proposed Yi-point, in which we use Cauchy flight to update the solution, by implementing both the harmony and reversal concept of Yi Jing. The proposed Yi-point balances both the effort of exploration and exploitation in the optimization process. To examine YI, we use the IEEE CEC 2017 benchmarks and compare YI against the dynamical YYPO, CV1.0 optimizer, and four classical optimizers, i.e., the differential evolution, the genetic algorithm, the particle swarm optimization, and the simulated annealing. According to the experimental results, YI shows highly competitive performance while keeping the low time complexity. The results of this work have implications for enhancing a meta-heuristic optimizer using the philosophy of Yi Jing.

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Section: Introductionmentioning

confidence: 99%

Meta-Heuristic Optimizer Inspired by the Philosophy of Yi Jing

Tang

Cai

Goh

2022

Preprint

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“…The technique is advantageous of low time complexity and being competitive in optimization problems. It has been successfully implemented in some real-time optimization problems, in which the low time-complexity of the algorithm is important [13,14,15,16].…”

Section: Introductionmentioning

confidence: 99%

A Novel Non-population-based Meta-heuristic Optimizer Inspired by the Philosophy of Yi Jing

Tang,

Goh

2021

Preprint

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Drawing inspiration from the philosophy of Yi Jing, Yin-Yang pair optimization (YYPO) has been shown to achieve competitive performance in single objective optimizations. Besides, it has the advantage of low time complexity when comparing to other population-based optimization. As a conceptual extension of YYPO, we proposed the novel Yi optimization (YI) algorithm as one of the best non-population-based optimizer. Incorporating both the harmony and reversal concept of Yi Jing, we replace the Yin-Yang pair with a Yi-point, in which we utilize the Lévy flight to update the solution and balance both the effort of the exploration and the exploitation in the optimization process. As a conceptual prototype, we examine YI with IEEE CEC 2017 benchmark and compare its performance with a Lévy flight-based optimizer CV1.0, the state-of-the-art dynamical Yin-Yang pair optimization in YYPO family and a few classical optimizers. According to the experimental results, YI shows highly competitive performance while keeping the low time complexity. Hence, the results of this work have implications for enhancing meta-heuristic optimizer using the philosophy of Yi Jing, which deserves research attention.

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