Island bat algorithm for optimization. (1st October 2018) - Al-Betar, Mohammed Azmi; Awadallah, Mohammed A.
Highlights: This paper proposes island bat algorithm ( i BA) for optimization problems. i BA is evaluated using IEEE-CEC2005 functions of different types and complexity. Experimental results suggest that i BA is not highly sensitive to its parameters. Comparative evaluation shows the superiority of i BA over 17 comparative methods. i BA is utilized for Economic Load Dispatch as a real-world application. Abstract: Structured population in evolutionary algorithms is a vital strategy to control diversity during the search. One of the most popular structured population strategies is the island model in which the population is divided into several sub-populations (islands). The EA normally search for each island independently. After a number of predefined iterations, a migration process is activated to exchange specific migrants between islands. Recently, bat-inspired algorithm has been proposed as a population-based algorithm to mimic the echolocation system involved in micro-bat. The main drawback of bat-inspired algorithm is its inability to preserve the diversity during the search and thus the prematurity can take place. In this paper, the strategy of island model is adapted for bat-inspired algorithm to empower its capability in controlling its diversity concepts. The proposed island bat-inspired algorithm is evaluated using 25 IEEE-CEC2005 benchmark functions with different size and complexity. The sensitivity analysis for the main parameters of island bat-inspired algorithm is well-studied to show their effect on the convergence properties. For comparative evaluation, island bat-inspired algorithm is compared with 17 competitive methods and shows very successful outcomes. Furthermore, the proposed algorithm is applied for three real-world cases of economic load dispatch problem where the results obtained prove considerable efficiency in comparison with other state-of-the-art methods.