Abstract
The krill herd (KH) algorithm is a global metaheuristic algorithm that was initially proposed for solving continuous optimization problems. The KH algorithm, since inception, has generated considerable real-world application interests in the research community. The standard algorithm solution implementation steps follow the initialization mechanism, which relies mainly on educated guesses or random initialization solution generation. Therefore, to improve the performance of the KH algorithm, the current study is set to investigate the influence of initializing the KH algorithm with three low-discrepancy sequences, such as the Faure sequence, Sobol sequence, and Van der Corput sequence. These low-discrepancy sequences are known to be more uniformly distributed across the problem search space than the commonly used random number initialization method. The study also evaluates the influence of population size on the performance of the proposed variants of the improved KH algorithms. The experimental results show significant improvements for the enhanced KH algorithms in terms of performance and the quality of solutions obtained; particularly on standard benchmarked high-dimension test problem instances, where the enhanced KH variants outperformed the existing basic KH algorithm for all the test functions evaluated. Similarly, the results for low dimension test cases showed less sensitivity to the initialization schemes, as the performance of our proposed improved scheme was comparable to that of the basic KH algorithm. However, in most cases, as the problem dimension was scaled up, the enhanced KH outperformed the basic KH. Evaluation results based on the population size of the algorithm, revealed that when the number of Krill is set at 25, the Sobol based KH initialization scheme performed better than did the other methods. Although, the Van der Corput and Faure based KH initialization schemes showed similar sensitivity when the dimension was set at 20. As we varied the population size of Krill, it was observed that the performance of the Sobol based KH initialization scheme deteriorated, whereas the other two methods showed superior performance. Overall, the findings from this study revealed that there are significant improvements in the performance of KH algorithm when initialized with low-discrepancy sequences.
Highlights
In the krill herd algorithm, the dispersion of the initial generators use a uniform probability distribution to
Previous motivate us to carry out our study on the three studies have shown that low-discrepancy initialization methods mentioned above and to sequences have greatly improved the performance of evaluate their influence on the krill herd (KH) algorithmic metaheuristic algorithms
The current study is set to investigate a similar To the best of our knowledge, no study has used a influence of three quasi-random sequence low-discrepancy sequence ( Van der initialization schemes on the krill herd algorithm Corput, Sobol or Faure) to improve the performance (KH)
Summary
In the krill herd algorithm, the dispersion of the initial generators use a uniform probability distribution to. The current study is set to investigate a similar To the best of our knowledge, no study has used a influence of three quasi-random sequence low-discrepancy sequence ( Van der initialization schemes on the krill herd algorithm Corput, Sobol or Faure) to improve the performance (KH). These initialization schemes are abbreviated in of KH. The numerical experimentation results, which are based on the improvement of the KH algorithm by modifying its initialization method using three low-discrepancy sequences, namely VcKH, FaKH, and SoKH, show that under the same parameter condition of population size, the maximum number of iterations, and replications, the proposed enhanced KH algorithm variants perform. Algorithm, their results showed that all maxima were successfully located for all the simulation runs
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