A novel enhanced global exploration whale optimization algorithm based on Lévy flights and judgment mechanism for global continuous optimization problems

Liu, Jianxun; Shi, Jinfei; Hao, Fei; Dai, Min

doi:10.1007/s00366-022-01638-1

Cited by 20 publications

(11 citation statements)

References 38 publications

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“…The WOA algorithm, like other metaheuristic algorithms, has drawbacks and limitations that make it less accurate and perform satisfactorily in some optimization problems. Experimental analysis and critical reviews [ 63 , 64 , 72 ] show that WOA suffers from poor search strategies [ 55 ], which leads to premature convergence [ 56 ], an unbalancing issue [ 57 ], stagnation far from local optima [ 58 ], and low population diversity [ 59 ]. Nevertheless, as shown in Fig.…”

Section: Different Approaches To Developing Woamentioning

confidence: 99%

“…The EGE-WOA algorithm [ 56 ] improves the convergence behavior of WOA based on convergent dual adaptive weights and enhances global discovery efficiency based on judging the predation status mechanism. in addition, the EGE-WOA performs the Lévy flight technique to improve global searchability.…”

Section: Improved Woa Variantsmentioning

confidence: 99%

“…Therefore, many SI algorithms have been developed to solve real-world continuous [ 40 – 43 ] and binary [ 44 – 48 ] optimization problems in different applications, such as the medical [ 49 , 50 ] and engineering [ 51 – 54 ] fields. However, SI algorithms suffer from inferior search strategies [ 55 ], which leads to premature convergence [ 56 ], an unbalancing issue [ 57 ], local optima trapping [ 58 ], and low population diversity [ 59 – 62 ].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Different Approaches To Developing Woamentioning

confidence: 99%

Section: Improved Woa Variantsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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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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“…REM-WOA has three optimized search mechanisms: surrounding prey, random search and spiral search [38]. Each iteration executes one search strategy according to the random variables.…”

Section: Search Strategymentioning

confidence: 99%

Directed Search Based on Improved Whale Optimization Algorithm for Test Case Prioritization

Yang¹,

Li²,

Xing³

et al. 2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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With the advent of the information age, the iterative speed of software update is gradually accelerating which makes software development severely limited by software testing. Test case prioritization is an effective way to accelerate software testing progress. With the introduction of heuristic algorithm to this task, the processing efficiency of test cases has been greatly improved. However, to overcome the shortcomings of slow convergence speed and easy fall into local optimum, the improved whale optimization algorithm is proposed for test case prioritization. Firstly, a model called n-dimensional directed search space is established for the swarm intelligence algorithm. Secondly, the enhanced whale optimization algorithm is applied to test case prioritization while the backtracking behavior is conducted for individuals when hitting the wall. In addition, a separate storage space for Pareto second optimization is also designed to filter the optimal solutions of the multi-objective tasks. Finally, both single-objective and multi-objective optimization experiments are carried out for open source projects and real-world projects, respectively. The results show that the improved whale optimization algorithm using n-dimensional directed search space is more conducive to the decisions of test case prioritization with fast convergence speed.

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“…Nadimi-Shahraki et al [32] adopted Levy motion and Brownian motion to achieve a better balance between the exploration and exploitation of whale optimization algorithm. Liu et al [33] proposed an enhanced global exploration whale optimization algorithm (EGE-WOA) for complex multi-dimensional problems. EGE-WOA employed Levy flights to improve the ability of its global exploration, and adopted new convergent dual adaptive weights to perfect the convergence procedure.…”

Section: Introductionmentioning

confidence: 99%

An Efficient and Robust Improved Whale Optimization Algorithm for Large Scale Global Optimization Problems

2022

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As an efficient meta-heuristic algorithm, the whale optimization algorithm (WOA) has been extensively applied to practical problems. However, WOA still has the drawbacks of converging slowly, and jumping out from extreme points especially for large scale optimization problems. To overcome these defects, a modified whale optimization algorithm integrated with a crisscross optimization algorithm (MWOA-CS) is proposed. In MWOA-CS, each dimension of the optimization problem updates its position by randomly performing improved WOA or crisscross optimization algorithm during the entire iterative process. The improved WOA adopts the new nonlinear convergence factor and nonlinear inertia weight to tune the ability of exploitation and exploration. To analyze the performance of MWOA-CS, a series of numerical experiments were performed on 30 test benchmark functions with dimension ranging from 300 to 1000. The experimental results revealed that the presented MWOA-CS provided better convergence speed and accuracy, and meanwhile, displayed a significantly more effective and robust performance than the original WOA and other state of the art meta-heuristic algorithms for solving large scale global optimization problems.

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A novel enhanced global exploration whale optimization algorithm based on Lévy flights and judgment mechanism for global continuous optimization problems

Cited by 20 publications

References 38 publications

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

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

Directed Search Based on Improved Whale Optimization Algorithm for Test Case Prioritization

An Efficient and Robust Improved Whale Optimization Algorithm for Large Scale Global Optimization Problems

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