SIWO: A Hybrid Algorithm Combined with the Conventional SCE and Novel IWO
Abstract:This paper develops a novel population-based optimization method, designated as the Shuffled Invasive Weed Optimization (SIWO). As a cooperative search metaphor inspired by natural memetics, it explores a global optimal solution for hard combinatorial optimization problems by combining the benefits of the competitiveness mixing of information of the shuffled complex evolution technique with the natural biological evolution-based Invasive Weed Optimization (IWO) algorithm. The algorithm consists of a set of interacting weed population partitioned into different memeplexes. It performs simultaneously an independent local search, that is completed by using an IWO method in each memeplex, and a shuffling strategy that allows for the exchange of information between local searches to move toward a global optimum in a technique similar to that used in the shuffled complex evolution algorithm. To show the feasibility, the efficiency and the effectiveness of the SIWO method, numerous simulations are conducted to test it using some benchmark problems. The achieved results of the simulation are compared with PSO algorithm and IWO algorithm, suggesting that SIWO has stable robust behavior on explored tests.
Document Type: Research Article
Publication date: 2007-11-01
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