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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. [ABSTRACT FROM AUTHOR]

The selection methods of population-based metaheuristics provide the driving force to generate good solutions. These selection methods select the individuals with a higher fitness to be members of the population in the next iteration correspond to the natural rule of Darwin's principle survival-of-the-fittest. Harmony search algorithm is a population-based metaheuristic, which mimicking the musical improvisation process where a group of musicians play the pitches of their musical instruments seeking for a pleasing harmony. It improvises the new harmony based on three rules: memory consideration, random consideration, and pitch adjustment. In this paper, we investigate the replacement of the original random selection of memory consideration with a set of selection methods in order to speed-up the convergence. These selection methods include tournament, proportional, and liner rank of Genetic Algorithm, and Global-best of Particle Swarm Optimization. The proposed harmony search with the different memory consideration selection methods evaluated using standard dataset published in the first International Nurse Rostering Competition INRC2010. Nurse rostering problem is a combinatorial optimization problem tackled by assigning a set of nurses with different skills to a set of shifts over predefined scheduling period. Experimentally, the tournament memory consideration selection method achieved the best rate of convergence as well as the best results in comparison with the other memory consideration selection methods. [ABSTRACT FROM AUTHOR]