Abstract
As a novel meta-heuristic algorithm, the Whale Optimization Algorithm (WOA) has well performance in solving optimization problems. However, WOA usually tends to trap in local optimal and it suffers slow convergence speed for large-scale and high-dimension optimization problems. A modified whale optimization algorithm with single-dimensional swimming (abbreviated as SWWOA) is proposed in order to overcome the shortcoming. First, tent map is applied to generate the initialize population for maximize search ability. Second, quasi-opposition learning is adopted after every iteration for further improving the search ability. Third, a novel nonlinearly control parameter factor that is based on logarithm function is presented in order to balance exploration and exploitation. Additionally, the last, single-dimensional swimming is proposed in order to replace the prey behaviour in standard WOA for tuning. The simulation experiments were conducted on 20 well-known benchmark functions. The results show that the proposed SWWOA has better performance in solution precision and higher convergence speed than the comparison methods.
Highlights
With the development of technology, increasing global optimization problems have to be solved in various fields, such as economic scheduling, aerospace, signal processing, artificial intelligence, mechanical design, chemical engineering [1,2,3], etc
A modified Whale Optimization Algorithm (WOA) algorithm SWWOA based on single-dimensional swimming is proposed, with four main improvements: (1) the use of tent chaotic sequences to optimize the quality of the initial population; (2) the introduction of quasi-opposition learning mechanism, any agent updated position will be learned by quasi-opposition learning, retain the better agent by fitness; (3) the use of logarithmic function to dynamically update the weights, instead of the original linear weights
The artificial bee colony optimization (ABC) algorithm is a much studied algorithm with high solution quality, which is selected in this paper, because SWWOA draws on its scout bee update mechanism
Summary
With the development of technology, increasing global optimization problems have to be solved in various fields, such as economic scheduling, aerospace, signal processing, artificial intelligence, mechanical design, chemical engineering [1,2,3], etc. The literature [34] introduced quantum behavior in the standard WOA to simulate the hunting process of humpback whales in order to enhance the search capability of the algorithm, which is used for feature selection These studies have improved the standard WOA algorithm to some extent, there are still problems of slow convergence and low solution accuracy, especially for high-dimensional large-scale optimization problems. A modified WOA algorithm SWWOA based on single-dimensional swimming is proposed, with four main improvements: (1) the use of tent chaotic sequences to optimize the quality of the initial population; (2) the introduction of quasi-opposition learning mechanism, any agent updated position will be learned by quasi-opposition learning, retain the better agent by fitness; (3) the use of logarithmic function to dynamically update the weights, instead of the original linear weights.
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