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An Improved BAT Algorithm Using Density-Based Clustering
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DOI：10.4114/intartif.vol26iss72pp102-123.png)
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Abstract
En 中文
BAT algorithm is a nature-inspired metaheuristic algorithm that depends on the principle of the echolocation behavior of bats. However, due to poor exploration, the algorithm suffers from being stuck in the local optima. An improved BAT algorithm based on the density-based clustering technique is proposed to enhance the algorithm's performance. In this paper, the initial population is improved by generating two populations, randomly and depending on the clusters' center information, and by getting the fittest individuals from these two populations, the initial improved one is generated. The random walk function is improved using chaotic maps instead of the fixed-size movement, so the local search is improved, as well as the global search abilities, by diversifying the solutions. Another improvement is dealing with stagnation by partitioning the search space into two parts depending on the generated clusters' information to obtain the newly generated solution, comparing their quality with the previously generated solution, and choosing the best. The performance of the proposed improved BAT algorithm is evaluated by comparing it with the original BAT algorithm over ten benchmark optimization test functions. Depending on the results, the improved BAT outperforms the original BAT by obtaining the optimal global solutions for most of the benchmark test functions.
Keywords:
Metaheuristic Algorithms
BAT Algorithm
Density-based Clustering
Chaotic Strategies
Journal
I
IF:
3.4
Papers:
71
Citations:
408