Quantum Particle Swarm With Teamwork Evolutionary Strategy for Multi-Objective Optimization on Electro-Optical Platform

This paper deals with multi-objective optimization design of the airborne electro-optical platform to reduce its mechanical resonance, improve its stability and reduce its mass. The traditional group intelligent algorithm could easily fall into a local optimum. This greatly affects its search accuracy; the multi-objective optimization of the optoelectronic platform cannot meet the design requirements with traditional algorithms. This paper proposes a teamwork evolutionary strategy quantum particle swarm optimization (TEQPSO) algorithm for balancing global and local search. This algorithm is based on a novel learning strategy consisting of cross-sequential quadratic programming and Gaussian chaotic mutation operators. The former performs the local search on the sample and the interlaced operation on the parent individual while the descendants of the latter generated by Gaussian chaotic mutation may produce new regions in the search space. The experiments performed on multimodal test and composite functions with or without coordinate rotation demonstrated that the population information could be utilized by the TEQPSO algorithm more effectively compared with the twelve QSOs and PSOs variants. This improves the algorithm performance, significantly. Finally, the TEQPSO algorithm is employed for multi-objective optimization design of the airborne electro-optical platform. This leads to significant vibration response and mass reduction as well as stiffness characteristics improvement. Finally, higher search accuracy and superior performance are obtained with the TEQPSO algorithm compared with the QPSO algorithm.