A modified whale optimization algorithm with multi-strategy mechanism for global optimization problems

Li, Mingyuan; Yu, Xiaobing; Fu, Bingbing; Wang, Xüming

doi:10.1007/s00521-023-08287-5

Cited by 11 publications

(8 citation statements)

References 48 publications

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“…They have their own update formulas. The Levy flight mechanism has been introduced to expand the search range of prospectors [13]. The updated model is as follows:…”

Section: Wsn Coverage Modelmentioning

confidence: 99%

“…They have their own update formulas. The Levy flight mechanism has been introduced to expand the search range of prospectors [13]. The updated model is as follows:

\begin{equation}{X}_p(t + 1) = {X}_i(t) + {A}_1 \cdot \left( {2{r}_2 \cdot {X}_{best}(t) - {X}_i(t)} \right) \cdot Levy\end{equation}

\begin{equation}{A}_1 = 1 + \left( {{r}_1 - 0.5} \right) \cdot \left( {{{\left( {\frac{{T - t}}{{T - 1}}} \right)}}^{2 - 1/T} + \frac{1}{T}} \right)\end{equation}

\begin{equation}Levy = 0.01 \cdot {\alpha }_1 \cdot \sigma \cdot \left| {{\alpha }_2^{ - 1/\beta }} \right|\end{equation}

$$\begin{equation}\sigma = {\left( {\left( {\Gamma (1 + ...

…”

Section: Multi‐strategy Gold Rush Optimizermentioning

confidence: 99%

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Wireless sensor network coverage optimization based on novel multi‐strategy gold rush optimizer

Kong,

Cai,

et al. 2024

Electronics Letters

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In Wireless Sensor Networks (WSN), there are three conflicting important issues, including extensive coverage, low node redundancy, and energy consumption for secondary deployment. To balance network coverage and longevity, a WSN coverage model with three objectives is developed. A novel multi‐strategy gold rush optimizer is proposed for the coverage optimization problem in WSN. First, the fusion search method becomes extensive and flexible by combining the migration strategy with the Levy flight mechanism and joining a follower group. Then, the dynamic opposite learning strategy is used to increase population diversity in the panning stage. In addition, a multi‐player collaborative strategy is utilized to improve search ability of the algorithm in the collaboration stage. Finally, the random differential mutation method is used to try to assist the algorithm in escaping local optima. The simulation results show that this algorithm compared with other algorithms performs better on all three objectives considered.

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“…They have their own update formulas. The Levy flight mechanism has been introduced to expand the search range of prospectors [13]. The updated model is as follows:…”

Section: Wsn Coverage Modelmentioning

confidence: 99%

“…They have their own update formulas. The Levy flight mechanism has been introduced to expand the search range of prospectors [13]. The updated model is as follows:

\begin{equation}{X}_p(t + 1) = {X}_i(t) + {A}_1 \cdot \left( {2{r}_2 \cdot {X}_{best}(t) - {X}_i(t)} \right) \cdot Levy\end{equation}

\begin{equation}{A}_1 = 1 + \left( {{r}_1 - 0.5} \right) \cdot \left( {{{\left( {\frac{{T - t}}{{T - 1}}} \right)}}^{2 - 1/T} + \frac{1}{T}} \right)\end{equation}

\begin{equation}Levy = 0.01 \cdot {\alpha }_1 \cdot \sigma \cdot \left| {{\alpha }_2^{ - 1/\beta }} \right|\end{equation}

$$\begin{equation}\sigma = {\left( {\left( {\Gamma (1 + ...

…”

Section: Multi‐strategy Gold Rush Optimizermentioning

confidence: 99%

Wireless sensor network coverage optimization based on novel multi‐strategy gold rush optimizer

Kong,

Cai,

et al. 2024

Electronics Letters

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“…The findings demonstrate that IMWOA outperforms other algorithms regarding photovoltaic output power prediction accuracy. Li et al [ 164 ] proposed the modified WOA (MWOA) algorithm with a multi-strategy mechanism to solve numerical optimization problems. The MWOA algorithm uses the elite reverse learning strategy, nonlinear convergence factor, DE/rand/1 mutation strategy, and Lévy flight disturbance strategy to improve convergence speed and balance between search strategies and avoid local optima trapping.…”

Section: Improved Woa Variantsmentioning

confidence: 99%

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

Nadimi-Shahraki

Zamani

Varzaneh

et al. 2023

Arch Computat Methods Eng

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Despite the simplicity of the whale optimization algorithm (WOA) and its success in solving some optimization problems, it faces many issues. Thus, WOA has attracted scholars' attention, and researchers frequently prefer to employ and improve it to address real-world application optimization problems. As a result, many WOA variations have been developed, usually using two main approaches improvement and hybridization. However, no comprehensive study critically reviews and analyzes WOA and its variants to find effective techniques and algorithms and develop more successful variants. Therefore, in this paper, first, the WOA is critically analyzed, then the last 5 years' developments of WOA are systematically reviewed. To do this, a new adapted PRISMA methodology is introduced to select eligible papers, including three main stages: identification, evaluation, and reporting. The evaluation stage was improved using three screening steps and strict inclusion criteria to select a reasonable number of eligible papers. Ultimately, 59 improved WOA and 57 hybrid WOA variants published by reputable publishers, including Springer, Elsevier, and IEEE, were selected as eligible papers. Effective techniques for improving and successful algorithms for hybridizing eligible WOA variants are described. The eligible WOA are reviewed in continuous, binary, single-objective, and multi/many-objective categories. The distribution of eligible WOA variants regarding their publisher, journal, application, and authors' country was visualized. It is also concluded that most papers in this area lack a comprehensive comparison with previous WOA variants and are usually compared only with other algorithms. Finally, some future directions are suggested.

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“…Tis paper also introduces many optimization algorithms. Li et al [38] proposed a modifed whale optimization algorithm (MWOA) with multistrategy mechanism. MWOA improved the initialization, control parameters, and search strategy of WOA.…”

Section: Related Workmentioning

confidence: 99%

CMAIS-WOA: An Improved WOA with Chaotic Mapping and Adaptive Iterative Strategy

Hsieh,

Zhang,

et al. 2023

Discrete Dynamics in Nature and Society

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This paper proposes an improved whale optimization algorithm with chaotic mapping and adaptive iteration strategy (CMAIS-WOA). This algorithm addresses the issues of the WOA algorithm that is prone to local optimal solutions with low stability. CMAIS-WOA utilizes chaotic mapping to enhance the diversity and coverage of the initial population. Also, it adaptively adjusts the weight values based on the current distribution of whale populations and the fitness of search agents. In addition, CMAIS-WOA uses an improved nonlinear convergence factor to adjust the breadth-first and depth-first search during the optimization process. The performance of the proposed CMAIS-WOA is evaluated by using 13 classical benchmark functions and IEEE CEC2014 test suite. The experimental results show that CMAIS-WOA effectively improves the stability of the optimal solution and helps the algorithm to approach the global optimal solution. The method proposed in this paper contributes to the field of optimization which solves problems more powerfully and efficiently.

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A modified whale optimization algorithm with multi-strategy mechanism for global optimization problems

Cited by 11 publications

References 48 publications

Wireless sensor network coverage optimization based on novel multi‐strategy gold rush optimizer

Wireless sensor network coverage optimization based on novel multi‐strategy gold rush optimizer

A Systematic Review of the Whale Optimization Algorithm: Theoretical Foundation, Improvements, and Hybridizations

CMAIS-WOA: An Improved WOA with Chaotic Mapping and Adaptive Iterative Strategy

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