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Discrete optimal sizing of truss using adaptive directional differential evolution
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 Title & Authors
Discrete optimal sizing of truss using adaptive directional differential evolution
Pham, Anh H.;
 
 Abstract
This article presents an adaptive directional differential evolution (ADDE) algorithm and its application in solving discrete sizing truss optimization problems. The algorithm is featured by a new self-adaptation approach and a simple directional strategy. In the adaptation approach, the mutation operator is adjusted in accordance with the change of population diversity, which can well balance between global exploration and local exploitation as well as locate the promising solutions. The directional strategy is based on the order relation between two difference solutions chosen for mutation and can bias the search direction for increasing the possibility of finding improved solutions. In addition, a new scaling factor is introduced as a vector of uniform random variables to maintain the diversity without crossover operation. Numerical results show that the optimal solutions of ADDE are as good as or better than those from some modern metaheuristics in the literature, while ADDE often uses fewer structural analyses.
 Keywords
adaptive directional differential evolution;population diversity;truss sizing optimization;discrete variables;
 Language
English
 Cited by
1.
Opposition Based Learning: A literature review, Swarm and Evolutionary Computation, 2017  crossref(new windwow)
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