Harmony Search Algorithm: Strengths and Weaknesses (original) (raw)
2013, Journal of Computer Engineering & Information Technology
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Cosine Harmony Search (CHS) for Static Optimization
International Journal on Advanced Science, Engineering and Information Technology, 2018
Harmony Search (HS) is the behaviour imitation of a musician looking for a balanced harmony. HS has difficulty finding the best tuning parameter, especially for Pitch Adjustment Rate (PAR). PAR plays a crucial role in selecting historical solution and adjusting it using Bandwidth (BW) value. PAR in HS requires a constant value to be initialized. Furthermore, there is a delay in convergence speed due to the disproportion of global and local search capabilities. Although some HS variants have claimed to overcome this shortcoming by introducing the self-modification of PAR, these justifications have been found to be imprecise and require more extensive experiments. Local Opposition-Based Learning Self-Adaptation Global Harmony Search (LHS) implements a heuristic factor, η for self-modification of PAR. It (η) manages the probability for selecting the adaptive step, either global adaptive step or worst adaptive step. If the value of η is large, the prospects of selecting the global adaptive step is higher, thereby allowing the algorithm to exploit a better harmony value. Conversely, if η is small, the worst adaptive step is prone to selection, therefore the algorithm is closed to the best global solution. In this paper, in addressing the existing HS obstacle, we introduce a Cosine Harmony Search (CHS) which incorporates an additional strategy rule. This additional strategy employs the η inspired by LHS and contains the cosine parameter. This allows for self-modification of pitch tuning to enlarge the exploitation capabilities. We test our proposed CHS on twelve standard static benchmark functions and compare it with basic HS and five state-of-the-art HS variants. Our proposed method and these state-of-the-art algorithms are executed using 30 and 50 dimensions. The numerical results demonstrated that the CHS has outperformed other state-of-the-art algorithms in terms of accuracy and convergence speed evaluations.
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