In this study, we propose a new constrained optimizationmethod using the quasi-chaotic optimization method (Q-COM) with the exact penalty function and the Sequential Quadratic Programming (SQP). The Q-COM, which has ...
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ISBN:
(纸本)9781457706530
In this study, we propose a new constrained optimizationmethod using the quasi-chaotic optimization method (Q-COM) with the exact penalty function and the Sequential Quadratic Programming (SQP). The Q-COM, which has been proposed recently, is a global optimizationmethod to solve unconstrained optimization problems in which the simultaneous perturbation gradient approximation is introduced into the chaotic optimization method to apply to a class of problems whose objective function values only can be computed. The SQP is well known and powerful constrained optimizationmethod to find local optimal solution. In the proposed method, the Q-COM with the exact penalty function is used as the global search method and the SQP is used as the local search method. We confirm the effectiveness of the proposed method through applications to various types of benchmark problems that include the coil spring design problem and the benchmark problems used in the special session on constrained real parameter optimization in CEC2006.
To overcome the deficiencies of weak local search ability in genetic algorithms (GA) and slow global convergence speed in ant colony optimization (ACO) algorithm in solving complex optimization problems, the chaotic o...
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To overcome the deficiencies of weak local search ability in genetic algorithms (GA) and slow global convergence speed in ant colony optimization (ACO) algorithm in solving complex optimization problems, the chaotic optimization method, multi-population collaborative strategy and adaptive control parameters are introduced into the GA and ACO algorithm to propose a genetic and ant colony adaptive collaborative optimization (MGACACO) algorithm for solving complex optimization problems The proposed MGACACO algorithm makes use of the exploration capability of GA and stochastic capability of ACO algorithm. In the proposed MGACACO algorithm, the multi-population strategy is used to realize the information exchange and cooperation among the various populations. The chaotic optimization method is used to overcome long search time, avoid falling into the local extremum and improve the search accuracy. The adaptive control parameters is used to make relatively uniform pheromone distribution, effectively solve the contradiction between expanding search and finding optimal solution. The collaborative strategy is used to dynamically balance the global ability and local search ability, and improve the convergence speed. Finally, various scale TSP are selected to verify the effectiveness of the proposed MGACACO algorithm. The experiment results show that the proposed MGACACO algorithm can avoid falling into the local extremum, and takes on better search precision and faster convergence speed.
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