Improved Whale Algorithm for Economic Load Dispatch Problem in Hydropower Plants and Comprehensive Performance Evaluation

Yang, Kun; Yang, Kun

doi:10.1007/s11269-022-03302-1

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…Later in the iteration, the magnitude of A, due to its own large step descent value, appears to be missing when it is necessary to mine hidden optimal solutions around the optimal solution. Therefore, inspired by the dynamic convergence factor in reference [20], this article proposes an improved nonlinear time-varying factor to improve a. This is used to compensate for the insufficient exploration ability in the early exploration phase of the the WOA and enhance its later development.…”

Section: Nonlinear Time-varying Factors and Inertia Weightsmentioning

confidence: 99%

“…Shahraki et al [19] proposed a unique pooling mechanism and combined it with three effective search strategies to propose a binary improved whale optimization algorithm, which effectively solved the problem of COVID-19 medical detection. Yang et al [20] introduced adaptive nonlinear inertial weights into the WOA to control the convergence rate, and adopted the finite mutation mechanism to improve the convergence efficiency, showing superior performance in economic load scheduling problems. Shen et al [21] showed that, combined with the multi-population evolution strategy, WOAs have a unique global search ability and excellent ability to jump out of local optima, and have excellent performance in various engineering design problems.…”

Section: Introductionmentioning

confidence: 99%

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Improved Whale Optimization Algorithm Based on Fusion Gravity Balance

Ouyang

Gong

Zhu

et al. 2023

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In order to improve the shortcomings of the whale optimization algorithm (WOA) in dealing with optimization problems, and further improve the accuracy and stability of the WOA, we propose an enhanced regenerative whale optimization algorithm based on gravity balance (GWOA). In the initial stage, the nonlinear time-varying factor and inertia weight strategy are introduced to change the foraging trajectory and exploration range, which improves the search efficiency and diversity. In the random walk stage and the encircling stage, the excellent solutions are protected by the gravitational balance strategy to ensure the high quality of solution. In order to prevent the algorithm from rapidly converging to the local extreme value and failing to jump out, a regeneration mechanism is introduced to help the whale population escape from the local optimal value, and to help the whale population find a better solution within the search interval through reasonable position updating. Compared with six algorithms on 16 benchmark functions, the contribution values of each strategy and Wilcoxon rank sum test show that GWOA performs well in 30-dimensional and 100-dimensional test functions and in practical applications. In general, GWOA has better optimization ability. In each algorithm contribution experiment, compared with the WOA, the indexes of the strategies added in each stage were improved. Finally, GWOA is applied to robot path planning and three classical engineering problems, and the stability and applicability of GWOA are verified.

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Section: Nonlinear Time-varying Factors and Inertia Weightsmentioning

confidence: 99%