حل تعارض در طراحی بهینه محصولات بر اساس ​​بهینه سازی ازدحام ذرات قابل تطبیق

To resolving the conflict in engineering optimization design of highly complex and nonlinear constraints, a new particle swarm optimization algorithm with adaptive inertia weight is proposed. In this algorithm, inertia weight is adaptively changed according to the current evolution speed and aggregation degree of the swarm, which provides the algorithm with dynamic adaptability, enhances the search ability and convergence performance of the algorithm. Finally, the validity of the algorithm is verified through an optimization example.

In collaborative product designs, the conflicts of collaborative product design are opposition and lack of agreement in design goals or variables determination among designer. Supposed that, Xj is the available design variable of designer Dj（Xj∈Qj）. Usually, design team expects the design is the optimal in each sub goal, that is We can know, as product and engineering design becomes more and more complicated, the objective function of optimization design is increasingly high dimensional, non-convex, and highly nonlinear.Therefore, finding a simple optimization method that can obtain global optimal quickly and effectively has an important significance to design conflict resolution and optimization. In recent years, particle swarm optimization (PSO) has been proven to be a better global optimization method with simple operation and parallel search[1]. PSO is a stochastic optimization technique motivated by the behavior of a flock of birds. It was first developed and introduced by Kennedy and Eberhart in 1995[2]. The algorithm is robust, well suited to handle non-linear, non-convex design problems. Recently, Because of its high convergence speed and relative simplicity, it has attracted a lot of attention from researchers and been successfully used to solve global optimization problems in engineering design[3,4,5]. But in PSO, particles demonstrate strong convergence character during searching process, leading to rapidly species diversity loss and easily premature convergence. Thus, how to choose a reasonable value of inertia weight is the key to solve this problem. Aiming at the problem, we improve PSO to provide a more efficient and effective design optimization method.

An improved particle swarm optimization with adaptive inertia weight is presented to optimize the engineering design. It takes inertia weight as the function of current evolution speed and aggregation degree of the swarm. The simulation results clearly show that the proposed method has faster convergence speed, and can get high quality solution than GA, BPSO and LDI-PSO.