A two‐phase harmony search algorithm for continuous optimization

Abstract

Harmony search (HS) algorithm is inspired by the music improvisation process in which a musician searches for the best harmony and continues to polish the harmony to improve its aesthetics. The efficiency of evolutionary algorithms depends on the extent of balance between diversification and intensification during the course of the search. An ideal evolutionary algorithm must have efficient exploration in the beginning and enhanced exploitation toward the end. In this paper, a two‐phase harmony search (TPHS) algorithm is proposed that attempts to strike a balance between exploration and exploitation by concentrating on diversification in the first phase using catastrophic mutation and then switches to intensification using local search in the second phase. The performance of TPHS is analyzed and compared with 4 state‐of‐the‐art HS variants on all the 30 IEEE CEC 2014 benchmark functions. The numerical results demonstrate the superiority of the proposed TPHS algorithm in terms of accuracy, particularly on multimodal functions when compared with other state‐of‐the‐art HS variants; further comparison with state‐of‐the‐art evolutionary algorithms reveals excellent performance of TPHS on composition functions. Composition functions are combined, rotated, shifted, and biased version of other unimodal and multimodal test functions and mimic the difficulties of real search spaces by providing a massive number of local optima and different shapes for different regions of the search space. The performance of the TPHS algorithm is also evaluated on a real‐life problem from the field of computer vision called camera calibration problem, ie, a 12‐dimensional highly nonlinear optimization problem with several local optima.

A Dynamic Adjusting Novel Global Harmony Search for Continuous Optimization Problems

“…However, the HS algorithm suffers a very serious limitation of premature convergence if one or more initially generated harmonies are in the vicinity of local optimal [21]. As Assad and Deep [22] point out, "The efficiency of evolutionary algorithms depends on the extent of balance between diversification and intensification during the course of the search. Intensification, also called exploitation, is the ability of an algorithm to exploit the search space in the vicinity of the current good solution, whereas diversification, also called exploration, is the process of exploring the new regions of a large search space and thus allows dissemination of the new information into the population.…”

An Improved Novel Global Harmony Search Algorithm Based on Selective Acceptance

“…Assad et al [19] pointed that the effectiveness of evolutionary algorithms relies on the extent of balance between diversification, which implies the ability of an algorithm to explore the high performance regions of the search space, and intensification, which is the ability of an algorithm to search for better solution in the high performance regions during the optimizing process. In other words, the key to improving the searching ability of evolutionary algorithms is to enhance the extent of balance between diversification and intensification capabilities.…”

Recent Advances in Harmony Search Algorithm

“…Another variant of HS based on the idea of increasing the PAR rather than decreasing it so as to favor exploration in the beginning of the algorithm has been proposed in [28]. A Two phase Harmony Search has been proposed in [29].…”