Abstract

An enhanced harmony search (EHS) algorithm is developed enabling the HS algorithm to quickly escape from local optima. For this purpose, the harmony memory updating phase is enhanced by considering also designs that are worse than the worst design stored in the harmony memory but are far enough from local optima. The proposed EHS algorithm is utilized to solve four classical weight minimization problems of steel frames. Results indicate that, as far as the quality of optimum design and convergence behavior are concerned, EHS is significantly superior or definitely competitive with other meta-heuristic optimization algorithms including the classical HS.

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