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1引言在自然科学和工程技术领域中,人们遇到的很多问题都可归结为目标优化问题,求解目标优化问题,经典的传统方法有:单纯形法、牛顿法、共轭梯度法、爬山法~([1])等.而在实际应用中,人们遇到的往往是些非线性、大规模的优化问题,传统方法难以求得最优解.近年来,群体智能算法成为一个研究的热点,遗传算法(GA)、粒子群算法(PSO)、蚁群算法(ACO)、人工萤火虫算法(GSO)~([2-5])等已广泛应用于求解目标优化问题,已有研究表明
1 Introduction In the field of natural sciences and engineering, many problems encountered by people can be attributed to the goal of optimizing the problem, the goal of solving optimization problems, the classic traditional methods are: simplex method, Newton’s method, conjugate gradient method, climbing method ~ ([1]), etc. In practical applications, people often encounter nonlinear and large-scale optimization problems, and the traditional methods are hard to find the optimal solution. In recent years, swarm intelligence algorithm has become a hot research topic , Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Ant Colony Algorithm (ACO) and Artificial Firefly Algorithm (GSO) ~ [2-5] have been widely used to solve the problem of objective optimization.