Convergence Analysis of a Class of Multi-Objective Quantum-Behaved Particle Swarm Optimization Algorithms and Its Application
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摘要: 针对ε支配容易丢失Pareto最优前沿边界点的不足， 提出了一种新支配关系——ε优势支配．基于ε 优势支配的最优粒子保留策略构建了一类多目标量子行为粒子群优化（CMOQPSO）算法的总体构架， 分析了这类算法在一定条件下的全局收敛性．将一种满足总体构架的多目标量子行为粒子群优化算法用于求解输电网规划问题， 结果表明这类多目标量子行为粒子群优化算法具有良好的全局寻优能力．Abstract: For the drawback of ε-dominance, a new dominance relationship, ε-superior dominance, is proposed to solve the problem of easy loss of boundary point of Pareto optimal front. An overall framework for a class of multi-objective quantum-behaved particle swarm optimization (CMOQPSO) algorithms is constructed with the preserving strategy of optimal particle based on ε -superior dominance, and the global convergence of this class of algorithms is analyzed under certain conditions. A multi-objective quantum-behaved particle swarm optimization algorithm under the overall framework is applied to solving the problem of power transmission network planning, and the results denote that this class of CMOQPSO algorithms have good ability of global optimization.
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