Abstract
Cuttlefish algorithm (CFA) is a metaheuristic bio-inspired algorithm that mimics the color-changing behavior by the cuttlefish. It is produced by light reflected from different layers of cells and involves two processes, i.e., reflection and visibility. The reflection process simulates the light reflection mechanism, while the visibility process simulates the visible appearance of the matching pattern used by the cuttlefish. There is no cooperation strategy between the solutions of the CFA's sub-populations. The strategy can improve the capabilities of local exploitation and global exploration in terms of solution diversity and quality during the search process. This paper introduces two schemes to improve the performance of the cuttlefish algorithm in continuous optimization problems. Firstly, a migration strategy is employed between the multi-population cuttlefish to increase solutions diversity during the search process. Secondly, one of the exploitation strategies of the standard cuttlefish is replaced with a new exploitation strategy based on short-term memory. The test demonstrates that the proposed algorithm outperforms the standard cuttlefish algorithm. Besides, the performance of the proposed algorithm was investigated using the CEC2013 benchmarking test functions. Comparisons with several state-of-the-art algorithms were performed, and the outcomes indicated that the proposed method offers a competitive performance advantage over the alternatives.
Highlights
Over the past years, an enormous number of metaheuristic algorithms have been introduced to solve optimization problems [10]
In this paper, we have modified the Cuttlefish Algorithm (CFA) by introducing two schemes to improve the performance in continuous optimization problems
A migration strategy is employed between the subpopulations to increase solutions diversity during the search process
Summary
An enormous number of metaheuristic algorithms have been introduced to solve optimization problems [10]. The works in [7], [53] discussed the importance of the solutions diversity in the initial population and during the search process. It is vital to undergone further examination of a cooperation method between the sub-populations in the CFA to increase the solutions diversity during the search process. There are only a few works that have attempted to develop memory-based algorithms [4], [9], [24]–[26], [42] It was suggested in [38] that memory can be beneficial for achieving solutions diversity and helping to increase the intensification process. Based on the above discussion, this paper aims to develop a migration-based cuttlefish algorithm with short-term memory to achieve diverse solutions and increase the solution quality. Once the fitness of each solution is calculated, the best solution (BestSolution) is kept to be updated and used
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