Equitable and Fair Performance Evaluation of Whale Optimization Algorithm

• URL: http://arxiv.org/abs/2310.07723v1
• Date: Mon, 4 Sep 2023 06:32:02 GMT
• Title: Equitable and Fair Performance Evaluation of Whale Optimization Algorithm
• Authors: Bryar A. Hassan, Tarik A. Rashid, Aram Ahmed, Shko M. Qader, Jaffer Majidpour, Mohmad Hussein Abdalla, Noor Tayfor, Hozan K. Hamarashid, Haval Sidqi, Kaniaw A. Noori
• Abstract summary: It is essential that all algorithms are exhaustively, somewhat, and intelligently evaluated. evaluating the effectiveness of optimization algorithms equitably and fairly is not an easy process for various reasons.
• Score: 4.0814527055582746
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: It is essential that all algorithms are exhaustively, somewhat, and intelligently evaluated. Nonetheless, evaluating the effectiveness of optimization algorithms equitably and fairly is not an easy process for various reasons. Choosing and initializing essential parameters, such as the size issues of the search area for each method and the number of iterations required to reduce the issues, might be particularly challenging. As a result, this chapter aims to contrast the Whale Optimization Algorithm (WOA) with the most recent algorithms on a selected set of benchmark problems with varying benchmark function hardness scores and initial control parameters comparable problem dimensions and search space. When solving a wide range of numerical optimization problems with varying difficulty scores, dimensions, and search areas, the experimental findings suggest that WOA may be statistically superior or inferior to the preceding algorithms referencing convergence speed, running time, and memory utilization.

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