A direct search algorithm is proposed for minimizing an arbitrary real valued function. The algorithm uses a new function transformation and three simplex-based operations. The function transformation provides global exploration features, while the simplex-based operations guarantees the termination of the algorithm and provides global convergence to a stationary point if the cost function is differentiable and its gradient is Lipschitz continuous. The algorithm’s performance has been extensively tested using benchmark functions and compared to some well-known global optimization algorithms. The results of the computational study show that the algorithm combines both simplicity and efficiency and is competitive with the heuristics-based strategies presently used for global optimization.

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function transformation simplexbased operations minimizing arbitrary real valued function direct search algorithm global optimization heuristicsbased strategies benchmark functions

,.A Direct Search Algorithm for Global Optimization. 9 (2),.

,"A Direct Search Algorithm for Global Optimization" 9

,"A Direct Search Algorithm for Global Optimization"