Various kinds of evolutionaryalgorithms have been developed to solve multi-objective optimization problems. One of them is multi-objective quantum-inspired evolutionary algorithm (MQEA) which utilizes quantum computi...
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ISBN:
(纸本)9781509006229
Various kinds of evolutionaryalgorithms have been developed to solve multi-objective optimization problems. One of them is multi-objective quantum-inspired evolutionary algorithm (MQEA) which utilizes quantum computing concepts to search the solution space effectively. MQEA used nondominated sorting and crowding distance calculation as the selection operator. This paper proposes MQEA with another kind of selection operator. The proposed RN-MQEA uses reference point-based nondominated sorting approach as the selection operator, which is adopted from NSGA-III. In the computer simulations, RN-MQEA is found to provide more diverse solutions compared to MQEA and NSGA-III in solving the DTLZ test problems.
This paper presents an improved multi-objective quantum-inspired evolutionary algorithm (IMQEA) for solving multi-objective optimization problems (MOPs). Different from general MQEAs, the proposed approach uses multip...
详细信息
ISBN:
(纸本)9781467317146
This paper presents an improved multi-objective quantum-inspired evolutionary algorithm (IMQEA) for solving multi-objective optimization problems (MOPs). Different from general MQEAs, the proposed approach uses multiple observations to yield candidate solutions. In the early stage of evolution, multiple observations of a given quantum bit (Q-bit) individual can yield solutions with good diversity, which is helpful for global search. In the later stage, most Q-bits have matured, and thus multiple observations of a given Q-bit individual are similar to a local search, which improves the accuracy of solutions. Experimental results for the multi-objective 0/1 knapsack problem show that the IMQEA finds solutions close to the Pareto-optimal front and maintains a good spread of the non-dominated set.
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