Harmony Search Algorithm: Proceedings of the 2nd by Joong Hoon Kim, Zong Woo Geem

By Joong Hoon Kim, Zong Woo Geem

The concord seek set of rules (HSA) is likely one of the such a lot famous concepts within the box of soppy computing, an incredible paradigm within the technological know-how and engineering group. This quantity, the court cases of the second foreign convention on concord seek set of rules 2015 (ICHSA 2015), brings jointly contributions describing the newest advancements within the box of sentimental computing with a different specialize in HSA concepts. It contains insurance of recent tools that experience very likely gigantic software in numerous fields.

Contributed articles hide facets of the next issues with regards to the concord seek set of rules: analytical experiences; more advantageous, hybrid and multi-objective editions; parameter tuning; and large-scale purposes. The booklet additionally includes papers discussing fresh advances at the following subject matters: genetic algorithms; evolutionary options; the firefly set of rules and cuckoo seek; particle swarm optimization and ant colony optimization; simulated annealing; and native seek techniques.

This booklet bargains a beneficial photo of the present prestige of the concord seek set of rules and comparable innovations, and should be an invaluable reference for practicing researchers and complex scholars in desktop technological know-how and engineering.

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Glover [20] further identified a template for SS. Martí et al. [21] provided the fundamental concepts and principles of SS. e. diversification generation method, improvement method, reference set update method, subset generation method and solution combination method. The implementation of SS is based on these methods. SS considers both intensification and diversification in search process, thus it has strong global search capability. SS has been successfully applied to solve the RACP [14,15]. Tabu search (TS) was initially proposed by Glover [22] and has been applied to many combinatorial optimization problems.

169–173, July 2002 4. : New Methodology, Harmony Search, its Robustness. In: GECCO Late Breaking Papers, pp. 174–178, July 2002 Harmony Search Algorithm with Ensemble of Surrogate Models 27 5. : Use of a harmony search for optimal design of coffer dam drainage pipes. J. KSCE 21(2-B), 119–128 (2001) 6. : A comprehensive survey of fitness approximation in evolutionary computation. Soft Computing 9(1), 3–12 (2005) 7. : DE-AEC: a differential evolution algorithm based on adaptive evolution control.

The term “low quality” means that the surrogate models are built with minimum number of samples that are produced during the evolution. Since the surrogate models are of low quality, we use an ensemble of surrogate models so that the probability of classifying the harmony vector as the one that can replace the worst one in the memory is high. In each generation, every original evaluation is preceded by an ensemble of surrogate function evaluations. In other words, if the produced harmony vector is said to be better than the worst one in the memory based on the surrogate function evaluations in the ensemble then the solution is evaluated using the actual function.

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