Multimodal optimization using whale optimization algorithm enhanced with local search and niching technique

Li, Hui; Zou, Peng; Huang, Zhiguo; Zeng, Chen; Liu, Xiao

doi:10.3934/mbe.2020001

Cited by 17 publications

(5 citation statements)

References 5 publications

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“…So we limit our review to the continuous optimization problems. Moreover, we only examine the NIO algorithms for the basic global optimization problems, and do not go into various more complex problems such as multi-modal optimization problems [5], dynamic optimization problems, and multi-objective optimization problems. This is done in order to keep the scope of our analysis in such a breadth where it is possible to examine each NIO algorithm in detail rather than superficially.…”

Section: B Scope and Review Methodologymentioning

confidence: 99%

Newly Emerging Nature-Inspired Optimization - Algorithm Review, Unified Framework, Evaluation, and Behavioural Parameter Optimization

Liu

Huang

et al. 2020

IEEE Access

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Nature-inspired optimization is a modern technique in the past decades. Researchers report their successful applications in various fields such as manufacturing, biomedical, and environmental engineering, while other researchers doubt its applicability. In this paper, we collect newly emerging nature-inspired optimization algorithms proposed after 2008, present them in a unified way, implement them, and evaluate them on benchmark functions. Moreover, we optimize the behavioural parameters for these algorithms. Since it is impossible to cover all interesting topics regarding nature-inspired optimization, this paper only focuses on the continuous encoding algorithms for single objective global problems, which is fundamental for other related topics.

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Section: B Scope and Review Methodologymentioning

confidence: 99%

Newly Emerging Nature-Inspired Optimization - Algorithm Review, Unified Framework, Evaluation, and Behavioural Parameter Optimization

Liu

Huang

et al. 2020

IEEE Access

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“…Sayed et al [123] introduced a chaotic search in the searching iterations of the WOA, to avoid entrapment in local optima and improve the convergence speed. H et al [124] proposed a multimodal WOA, which enhanced the multimodal search ability of the WOA by using the niching technique and improved the local search efficiency of the WOA by applying a Gaussian sampling technique. In [125], a novel hybrid WOA with gathering strategies (HWOAG) was proposed; it included an individual-based updating method, a random opposition learning strategy, and a grey wolf optimizer.…”

Section: E Research Progress Regarding Theory and Applications Of Wha...mentioning

confidence: 99%

A survey on several new popular swarm intelligence optimization algorithms

Wei

Huang

2023

Preprint

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Traditional optimization methods rely on parameter selection and a require high mathematical performance of the objective function. Thus, swarm intelligence optimization algorithms have attracted extensive attention as modern optimization methods in recent years, achieving remarkable results in the fields of machine learning, engineering optimization, process control, and elsewhere. Swarm intelligence optimization algorithms are a form of computing technology built upon the laws of biological group behavior, they are simple, fast, and place low requirements upon the objective functions. The traditional swarm intelligence algorithm offers new ideas for solving certain practical problems, however, it suffers from shortcomings in several experiments. In recent years, numerous scholars have proposed new swarm intelligence optimization algorithms, this paper selects several of the more typical swarm intelligence algorithms proposed in recent years at home and abroad, including the Whale Optimization Algorithm, Moth-Flame Optimization Algorithm, Fireworks Algorithm, Dragonfly Algorithm, Crow Search Algorithm, Butterfly Optimization Algorithm, and Pigeons Algorithm. Furthermore, the experimental performances of these algorithms are compared with respect to their convergence speed, accuracy, and stability, using 18 standard test functions, and the relative improvement methods are compared and analyzed. Finally, the characteristics of the swarm intelligence optimization algorithm are summarized, and its future development potential is discussed.

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“…Whales are a gregarious species that live in pods. In the search area, all of the whales use ultrasound to communicate with one another [28]. Each whale has a degree of computing capacity that allows it to compute the distance between itself and other whales.…”

Section: Whale Swarm Algorithmmentioning

confidence: 99%

Performance Improvement of Generative Adversarial Networks to Generate Digital Color Images of Skin Diseases

Ahmed,

Kashmola

2023

Iraqi Journal of Science

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The main task of creating new digital images of different skin diseases is to increase the resolution of the specific textures and colors of each skin disease. In this paper, the performance of generative adversarial networks has been optimized to generate multicolor and histological color digital images of a variety of skin diseases (melanoma, birthmarks, and basal cell carcinomas). Two architectures for generative adversarial networks were built using two models: the first is a model for generating new images of dermatology through training processes, and the second is a discrimination model whose main task is to identify the generated digital images as either real or fake. The gray wolf swarm algorithm and the whale swarm algorithm were relied on to generate values that improve the performance of GANs and insert them into the generator instead of random values, which in turn worked to reduce the loss values for the generated images. Loss values were adopted as a measure of optimizations for each epoch, and the fastest access time to actual digital images for each skin disease was adopted. Before the optimization operations, 50% accurate images of skin diseases were obtained; after the optimization operations, 98% accurate images of skin diseases were obtained.

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Multimodal optimization using whale optimization algorithm enhanced with local search and niching technique

Cited by 17 publications

References 5 publications

Newly Emerging Nature-Inspired Optimization - Algorithm Review, Unified Framework, Evaluation, and Behavioural Parameter Optimization

Newly Emerging Nature-Inspired Optimization - Algorithm Review, Unified Framework, Evaluation, and Behavioural Parameter Optimization

A survey on several new popular swarm intelligence optimization algorithms

Performance Improvement of Generative Adversarial Networks to Generate Digital Color Images of Skin Diseases

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