基于不可行度和内分泌原理的多目标粒子群方法

针对有约束条件的多目标优化问题，提出了一种求解带约束的基于内分泌思想的多目标粒子群算法。利用不可行度方法和约束主导原理指导进化过程中精英种群的选择操作和约束条件的处理，根据生物体激素调节机制中促激素和释放激素间的相互作用原理，考虑当前非劣解集中的个体对其最邻近的一类群体的监督控制，引入当前粒子的类全局最优位置来反映其所属类中最好位置粒子对当前粒子的影响。为验证多目标约束优化算法的有效性，对两个典型的多目标优化问题进行了仿真实验，仿真结果表明该算法能较大概率地获得多目标约束优化问题的可行Pareto最优解。

Multi-objective PSO based on infeasibility degree and principle of endocrine

For multi-objective optimization problems with constraint, a novel particle swarm optimization algorithm(PSO) for multi-objective with constraint is proposed. In the method, the constraints and the selection for elite swarm are disposed by infeasibility degree and domain principle. According to the control and supervised principle between simulating hormone(SH) and releasing hormone(RH) in endocrine system, and considering the supervision and control of individual in the set of non-dominated for the nearest class of swarm, the global optimization position of class is used to generate the new position for particles which are belonged to it, .In order to validate the effectiveness of given method, three benchmark multi-objective problems are simulated by the given method，NSGA-II and MOPSO-CD, the results indicated that the given method can find feasible Pareto solutions with a large probability.