Reduced Yin-Yang-Pair optimization and its performance on the CEC 2016 expensive case

Punnathanam, Varun; Kotecha, Prakash

doi:10.1109/cec.2016.7744168

Cited by 6 publications

(4 citation statements)

References 9 publications

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“…Inspired by the philosophy, Varun et al proposed the YYPO that makes use of the concept illustrated in Fig. 1(a) [8].…”

Section: Review Of the Yin-yang Pair Optimizationmentioning

confidence: 99%

“…One-way splitting is directional splitting using the vector with all positive element or all negative element, while D-way splitting use the vector with elements of random sign. The probabilities of choosing a splitting strategy have been optimized [8].…”

Section: Review Of the Yin-yang Pair Optimizationmentioning

confidence: 99%

“…Some of the ideas are sophisticated and inspired the development of meta-heuristic optimization techniques and related applications [5,6,7]. For example, the ancient Yin-Yang idea inspired the non-population-based Yin-Yang pair optimization (YYPO) [8,9,10,11,12]. The technique is advantageous of low time complexity and being competitive in optimization problems.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Inspired by the philosophy, Varun et al proposed the YYPO that makes use of the concept illustrated in Fig. 1(a) [8].…”

Section: Review Of the Yin-yang Pair Optimizationmentioning

confidence: 99%

Section: Review Of the Yin-yang Pair Optimizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Tang,

Goh

2021

Preprint

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“…In 2016, the Yin-Yang idea had inspired Punnathanam et al to develop the Yin-Yang pair optimization (YYPO) [8], [9]. The algorithm uses two points to form the Yin-Yang pair to perform the optimization task.…”

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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