Hybrid Particle Swarm Optimization Algorithm for Process Planning

Abstract

Process planning is a typical combinatorial optimization problem. When the scale of the problem increases, combinatorial explosion occurs, which makes it difficult for traditional precise algorithms to solve the problem. A hybrid particle swarm optimization (HPSO) algorithm is proposed in this paper to solve problems of process planning. A hierarchical coding method including operation layer, machine layer and logic layer is designed in this algorithm. Each layer of coding corresponds to the decision of a sub-problem of process planning. Several genetic operators of the genetic algorithm are designed to replace the update formula of particle position and velocity in the particle swarm optimization algorithm. The results of the benchmark example in case study show that the algorithm proposed in this paper has better performance.

In search of diverse and connected teams: A computational approach to assemble diverse teams based on members’ social networks

“…Moreover, PSO operates with a global optimum that does not exist in Pareto front problems. Zhang et al [ 76 ] proposed a hybrid version that replaces the PSO’s particle position and velocity update formulas with the genetic algorithm’s crossover and mutation operations. In a nutshell, the HPSO algorithm iteratively examines each particle and (a) applies the crossover step with a random non-dominated solution found by the particle, (b) applies the crossover step with a random non-dominated solution known from all the population, (c) and performs the mutation step.…”

“…Hybrid Particle Swarm Optimization (HPSO) method. This algorithm combines the steps of particle swarm optimization algorithms (PSO) and genetic algorithms (GA) [76]. In its original version, PSO starts with a population of candidate solutions (called particles) and moves them around in the search space over the particle's position and velocity.…”

A Resource Utilization Prediction Model for Cloud Data Centers Using Evolutionary Algorithms and Machine Learning Techniques

“…Hybrid versions of these algorithms have been used in a variety of domains and impressive results are achieved. The applications include antenna array pattern synthesis [6], mining association rules [7], forecasting electricity demand [8], scheduling resources in cloud computing [9], and process planning [10]. Authors in [11] have proved with the simulation that the hybrid version overcomes the disadvantages of the individual algorithms.…”

Chaos-Enhanced Adaptive Hybrid Butterfly Particle Swarm Optimization Algorithm for Passive Target Localization

“…Moreover, the combination of search ability of the FA and PSO algorithms is proposed in the HFPSO algorithm, to improve the convergence speed and solution accuracy [ 34 ]. Additionally, different hybridizations of the BOA algorithm with other EAs, such as ABC and BOA (BOA/ABC) [ 16 ], BOA with DE (HBODEA) [ 35 ] and BOA with PSO (HPSO) [ 36 ] can be found in the literature, which are successfully applied to solve different complex optimization problems. Therefore, the hybridization of the EAs is proven to enhance the speed of reaching optimal solution, so as to avoid the problem of prematurely converging to a solution and provide more precise solutions.…”