Fine-Grained Ensemble of Evolutionary Operators for Objective Space Partition Based Multi-Objective Optimization

In order to improve our military 's level of intelligent accusation decision-making in future intelligent joint warfare, this paper studies operation loop recommendation methods for kill web based on the fundamental combat form of the future, i.e., "web-based kill," and the operation loop theory. Firstly, we pioneer the operation loop recommendation problem with operation ring quality as the objective and closed-loop time as the constraint, and construct the corresponding planning model. Secondly, considering the case where there are multiple decision objectives for the combat ring recommendation problem, we propose for the first time a multi-objective optimization algorithm, the multi-objective ant colony evolutionary algorithm based on decomposition (MOACEA/D), which integrates the multi-objective evolutionary algorithm based on decomposition (MOEA/D) with the ant colony algorithm. The MOACEA/D can converge the optimal solutions of multiple single objectives nondominated solution set for the multi-objective problem. Finally, compared with other classical multi-objective optimization algorithms, the MOACEA/D is superior to other algorithms superior in terms of the hyper volume (HV), which verifies the effectiveness of the method and greatly improves the quality and efficiency of commanders' decision-making.

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Fine-Grained Ensemble of Evolutionary Operators for Objective Space Partition Based Multi-Objective Optimization

Cited by 3 publications

References 54 publications

Role division approach for firefly algorithm based on t-distribution perturbation and differential mutation

Role division approach for firefly algorithm based on t-distribution perturbation and differential mutation

Solving hybrid charging strategy electric vehicle based dynamic routing problem via evolutionary multi-objective optimization

Multi-objective optimization of operation loop recommendation for kill web

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