Abstract
A novel robust hybrid metaheuristic optimization approach, which can be considered as an improvement of the recently developed bat algorithm, is proposed to solve global numerical optimization problems. The improvement includes the addition of pitch adjustment operation in HS serving as a mutation operator during the process of the bat updating with the aim of speeding up convergence, thus making the approach more feasible for a wider range of real-world applications. The detailed implementation procedure for this improved metaheuristic method is also described. Fourteen standard benchmark functions are applied to verify the effects of these improvements, and it is demonstrated that, in most situations, the performance of this hybrid metaheuristic method (HS/BA) is superior to, or at least highly competitive with, the standard BA and other population-based optimization methods, such as ACO, BA, BBO, DE, ES, GA, HS, PSO, and SGA. The effect of the HS/BA parameters is also analyzed.
Highlights
The process of optimization is searching a vector in a function that produces an optimal solution
The optimization operators of harmony search (HS) algorithm are specified as the harmony memory (HM), which keeps the solution vectors which are all within the search space, as shown in (2); the harmony memory size HMS, which represents the number of solution vectors kept in the HM; the harmony memory consideration rate (HMCR); the pitch adjustment rate (PAR); the pitch adjustment bandwidth
This paper proposed a hybrid metaheuristic HS/bat algorithm (BA) method for optimization problem
Summary
The process of optimization is searching a vector in a function that produces an optimal solution. Nature-inspired metaheuristic algorithms perform powerfully and efficiently in solving modern nonlinear numerical global optimization problems. Proposed by Geem et al in 2001, harmony search (HS) [26] is a new metaheuristic approach for minimizing possibly nondifferentiable and nonlinear functions in continuous space. HS algorithm originates in the similarity between engineering optimization and music improvisation, and the engineers search for a global optimal solution as determined by an objective function, just like the musicians strive for finding aesthetic harmony as determined by aesthetician. We combine two approaches to propose a new hybrid metaheuristic algorithm according to the principle of HS and BA, and this improved BA method is used to search the optimal objective function value.
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