Multi-swarm optimization

About: Multi-swarm optimization is a research topic. Over the lifetime, 19162 publications have been published within this topic receiving 549725 citations.

Adaptive particle swarm optimization on individual level

Abstract: An adaptive particle swarm optimization (PSO) on individual level is presented. By analyzing the social model of PSO, a replacement criterion, based on the diversity of fitness between the current particle and the best historical experience, is introduced to maintain the social attribution of swarm adaptively by taking off inactive particles. The testing of three benchmark functions indicates that it improves the average performance effectively.

Parameter optimization of a multi-pass milling process using non-traditional optimization algorithms

Abstract: The effective optimization of machining process parameters affects dramatically the cost and production time of machined components as well as the quality of the final products. This paper presents optimization aspects of a multi-pass milling operation. The objective considered is minimization of production time (i.e. maximization of production rate) subjected to various constraints of arbor strength, arbor deflection, and cutting power. Various cutting strategies are considered to determine the optimal process parameters like the number of passes, depth of cut for each pass, cutting speed, and feed. The upper and lower bounds of the process parameters are also considered in the study. The optimization is carried out using three non-traditional optimization algorithms namely, artificial bee colony (ABC), particle swarm optimization (PSO), and simulated annealing (SA). An application example is presented and solved to illustrate the effectiveness of the presented algorithms. The results of the presented algorithms are compared with the previously published results obtained by using other optimization techniques.

The particle swarm: social adaptation in information-processing systems

Abstract: A clutch mechanism of the rocker-shift type has a carrier member interposed between and affixed to one of a rotatable driving and a rotatable driven member. The carrier member has circumferentially spaced rocker arms, having normally self-disengaging clutch portions, that are adapted to be rocked radially, on at least one end thereof, by means of a shift yoke and collar, for selective rocking engagement into coupling engagement with adjacent corresponding normally self-disengaging clutch portions on the other of the driving and driven members. The collar has locked neutral and locked engaged positions on the carrier, with the improvement comprising means for axially shifting the collar to an unlocked engaged position intermediate the locked neutral and locked engaged positions and yieldingly maintaining the collar in this unlocked engaged position at a predetermined torque level, with the collar being axially shifted back into locked neutral position as result of the separation of the self-disengaging clutch portions when the predetermined torque level is exceeded by the torque transfer between the driving and driven members.

Enhanced parallel cat swarm optimization based on the Taguchi method

Abstract: In this paper, we present an enhanced parallel cat swarm optimization (EPCSO) method for solving numerical optimization problems. The parallel cat swarm optimization (PCSO) method is an optimization algorithm designed to solve numerical optimization problems under the conditions of a small population size and a few iteration numbers. The Taguchi method is widely used in the industry for optimizing the product and the process conditions. By adopting the Taguchi method into the tracing mode process of the PCSO method, we propose the EPCSO method with better accuracy and less computational time. In this paper, five test functions are used to evaluate the accuracy of the proposed EPCSO method. The experimental results show that the proposed EPCSO method gets higher accuracies than the existing PSO-based methods and requires less computational time than the PCSO method. We also apply the proposed method to solve the aircraft schedule recovery problem. The experimental results show that the proposed EPCSO method can provide the optimum recovered aircraft schedule in a very short time. The proposed EPCSO method gets the same recovery schedule having the same total delay time, the same delayed flight numbers and the same number of long delay flights as the Liu, Chen, and Chou method (2009). The optimal solutions can be found by the proposed EPCSO method in a very short time.