Migration-Based Moth-Flame Optimization Algorithm

Nadimi-Shahraki, Mohammad H.; Fatahi, Ali; Zamani, Hoda; Mirjalili, Seyedali; Abualigah, Laith; Elaziz, Mohamed Abd

doi:10.3390/pr9122276

Cited by 54 publications

(17 citation statements)

References 112 publications

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“…Swarm-based approaches are developed and utilized in many areas of machine learning and artificial intelligence according to the cooperative intellect life of self-organized and reorganized coordination, e.g., artificial clusters of randomly generated agents. This way of problem solving is well-known among the swarm-intelligence community; due to such a delicate balance between the exploration and exploitation steps, it is a hard target to achieve [45][46][47].…”

Section: Evolutionarymentioning

confidence: 99%

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

et al. 2022

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The Harris hawk optimizer is a recent population-based metaheuristics algorithm that simulates the hunting behavior of hawks. This swarm-based optimizer performs the optimization procedure using a novel way of exploration and exploitation and the multiphases of search. In this review research, we focused on the applications and developments of the recent well-established robust optimizer Harris hawk optimizer (HHO) as one of the most popular swarm-based techniques of 2020. Moreover, several experiments were carried out to prove the powerfulness and effectivness of HHO compared with nine other state-of-art algorithms using Congress on Evolutionary Computation (CEC2005) and CEC2017. The literature review paper includes deep insight about possible future directions and possible ideas worth investigations regarding the new variants of the HHO algorithm and its widespread applications.

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Section: Evolutionarymentioning

confidence: 99%

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

et al. 2022

Self Cite

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“…In this regard, in this subsection, population diversity analysis of SSVUBA performance has been studied. To show the population diversity of SSVUBA in achieving the solution during the iterations of the algorithm, the I C index is used, which is calculated using Equations ( 7) and ( 8) [40].…”

Section: Population Diversity Analysismentioning

confidence: 99%

Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems

Dehghani

Trojovský

2022

Sensors

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With the advancement of science and technology, new complex optimization problems have emerged, and the achievement of optimal solutions has become increasingly important. Many of these problems have features and difficulties such as non-convex, nonlinear, discrete search space, and a non-differentiable objective function. Achieving the optimal solution to such problems has become a major challenge. To address this challenge and provide a solution to deal with the complexities and difficulties of optimization applications, a new stochastic-based optimization algorithm is proposed in this study. Optimization algorithms are a type of stochastic approach for addressing optimization issues that use random scanning of the search space to produce quasi-optimal answers. The Selecting Some Variables to Update-Based Algorithm (SSVUBA) is a new optimization algorithm developed in this study to handle optimization issues in various fields. The suggested algorithm’s key principles are to make better use of the information provided by different members of the population and to adjust the number of variables used to update the algorithm population during the iterations of the algorithm. The theory of the proposed SSVUBA is described, and then its mathematical model is offered for use in solving optimization issues. Fifty-three objective functions, including unimodal, multimodal, and CEC 2017 test functions, are utilized to assess the ability and usefulness of the proposed SSVUBA in addressing optimization issues. SSVUBA’s performance in optimizing real-world applications is evaluated on four engineering design issues. Furthermore, the performance of SSVUBA in optimization was compared to the performance of eight well-known algorithms to further evaluate its quality. The simulation results reveal that the proposed SSVUBA has a significant ability to handle various optimization issues and that it outperforms other competitor algorithms by giving appropriate quasi-optimal solutions that are closer to the global optima.

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“…Researchers try to use different evolutionary method like [ 20 – 23 ]. By reviewing the literature, we can summarize that there are roughly three types of variants of PSO algorithms.…”

Section: Related Workmentioning

confidence: 99%

A twinning bare bones particle swarm optimization algorithm

Guo

Shi

et al. 2022

PLoS ONE

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A twinning bare bones particle swarm optimization(TBBPSO) algorithm is proposed in this paper. The TBBPSO is combined by two operators, the twins grouping operator (TGO) and the merger operator (MO). The TGO aims at the reorganization of the particle swarm. Two particles will form as a twin and influence each other in subsequent iterations. In a twin, one particle is designed to do the global search while the other one is designed to do the local search. The MO aims at merging the twins and enhancing the search ability of the main group. Two operators work together to enhance the local minimum escaping ability of proposed methods. In addition, no parameter adjustment is needed in TBBPSO, which means TBBPSO can solve different types of optimization problems without previous information or parameter adjustment. In the benchmark functions test, the CEC2014 benchmark functions are used. Experimental results prove that proposed methods can present high precision results for various types of optimization problems.

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Migration-Based Moth-Flame Optimization Algorithm

Cited by 54 publications

References 112 publications

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

Recent Advances in Harris Hawks Optimization: A Comparative Study and Applications

Selecting Some Variables to Update-Based Algorithm for Solving Optimization Problems

A twinning bare bones particle swarm optimization algorithm

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