Diversity enhanced particle swarm optimization with neighborhood search

نویسندگان

  • Hui Wang
  • Hui Sun
  • Changhe Li
  • Shahryar Rahnamayan
  • Jeng-Shyang Pan
چکیده

a School of Information Engineering, Nanchang Institute of Technology, Nanchang 330099, PR China b School of Computer, China University of Geosciences, Wuhan 430072, PR China c Faculty of Engineering and Applied Science, University of Ontario Institute of Technology (UOIT), 2000 Simcoe Street North, Oshawa, ON, Canada L1H 7K4 d Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen 518055, PR China Department of Electronic Engineering, National Kaohsiung University of Applied Sciences, Kaohsiung 807, Taiwan

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Enhanced Comprehensive Learning Cooperative Particle Swarm Optimization with Fuzzy Inertia Weight (ECLCFPSO-IW)

So far various methods for optimization presented and one of most popular of them are optimization algorithms based on swarm intelligence and also one of most successful of them is Particle Swarm Optimization (PSO). Prior some efforts by applying fuzzy logic for improving defects of PSO such as trapping in local optimums and early convergence has been done. Moreover to overcome the problem of i...

متن کامل

Diversified Particle Swarm Optimization for Hybrid Flowshop Scheduling

The aim of this paper is to propose a new particle swarm optimization algorithm to solve a hybrid flowshop scheduling with sequence-dependent setup times problem, which is of great importance in the industrial context. This algorithm is called diversified particle swarm optimization algorithm which is a generalization of particle swarm optimization algorithm and inspired by an anarchic society ...

متن کامل

Fuzzy particle swarm optimization with nearest-better neighborhood for multimodal optimization

In the last decades, many efforts have been made to solve multimodal optimization problems using Particle Swarm Optimization (PSO). To produce good results, these PSO algorithms need to specify some niching parameters to define the local neighborhood. In this paper, our motivation is to propose the novel neighborhood structures that remove undesirable niching parameters without sacrificing perf...

متن کامل

Particle Swarm Optimization for the Orienteering Problem

In this paper, we propose a Particle Swarm Optimization (PSO) based solution to the Orienteering Problem. A major problem in many PSO applications is the premature convergence of some particles to local optimums. To overcome this problem in our application, such particles are re-initiated; consequently, the exploration capability of PSO is improved. Furthermore, by employing Reduced Variable Ne...

متن کامل

Chaotic-based Particle Swarm Optimization with Inertia Weight for Optimization Tasks

Among variety of meta-heuristic population-based search algorithms, particle swarm optimization (PSO) with adaptive inertia weight (AIW) has been considered as a versatile optimization tool, which incorporates the experience of the whole swarm into the movement of particles. Although the exploitation ability of this algorithm is great, it cannot comprehensively explore the search space and may ...

متن کامل

A hybrid Particle Swarm Optimization - Variable Neighborhood Search algorithm for Constrained Shortest Path problems

In this paper, a well known NP-hard problem, the Constrained Shortest Path problem, is studied. As efficient metaheuristic approaches are required for its solution, a new hybridized version of Particle Swarm Optimization algorithm with Variable Neighborhood Search is proposed for solving this significant combinatorial optimization problem. Particle Swarm Optimization (PSO) is a population-based...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:
  • Inf. Sci.

دوره 223  شماره 

صفحات  -

تاریخ انتشار 2013