Multi Population Ant Colony Optimization Algorithm Based On Congestion In order to solve the problems of slow convergence and low accuracy of the traditional ant colony algorithm in solving the traveling salesman problem (tsp), this paper proposes a heterogeneous ant colony algorithm based on the selective evolution mechanism and game strategy (sghaca). To address the lack of convergence speed and diversity of traditional ant colony algorithm when solving large scale travelling salesmen problem (tsp), a multi ant colony optimization.
Heterogeneous Ant Colony Algorithm Based On Selective Evolution This study proposes the multiple ant colony algorithm combining community relationship network (caco) by collecting route information of all ants and constructing a route relationship network to improve the accuracy of the solution. Heterogeneous ant colony algorithm based on selective evolution mechanism and game strategy. Heterogeneous ant colony algorithm based on selective evolution mechanism and game strategy. The proposed algorithm is used in solving the degenerate primer design problem. since the multi objective ant colony optimization algorithm consumes a massive cost of time and computation resources, improving its convergence performance is essential.
Evolution Curve Of Traditional Ant Colony Algorithm Download Heterogeneous ant colony algorithm based on selective evolution mechanism and game strategy. The proposed algorithm is used in solving the degenerate primer design problem. since the multi objective ant colony optimization algorithm consumes a massive cost of time and computation resources, improving its convergence performance is essential. In order to solve the problems of slow convergence and low accuracy of the tradi tional ant colony algorithm in solving the traveling salesman problem (tsp), this paper proposes a heterogeneous ant colony algorithm based on the selective evo lution mechanism and game strategy (sghaca ). Experimental results indicate that the proposed heterogeneous ant colony optimization based on adaptive interactive learning and non zero sum game has a higher quality solution and faster convergence speed in solving the traveling salesman problem.
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