Multiple Swarms Multi-Objective Particle Swarm Optimization Based on Decomposition
نویسندگان
چکیده
منابع مشابه
A Multi-Objective Hybrid Particle Swarm Optimization-based Service Identification
Service identification step is a basic requirement for a detailed design and implementation of services in a Service Oriented Architecture (SOA). Existing methods for service identification ignore the automation capability while providing human based prescriptive guidelines, which mostly are not applicable at enterprise scales. In this paper, we propose a top down approach to identify automatic...
متن کاملGPU-Based Parallel Multi-objective Particle Swarm Optimization
In the recent years, multi-objective particle swarm optimization (MOPSO) has become quite popular in the field of multi-objective optimization. However, due to a large amount of fitness evaluations as well as the task of archive maintaining, the running time of MOPSO for optimizing some difficult problems may be quite long. This paper proposes a parallel MOPSO based on consumer-level Graphics P...
متن کاملR2-Based Multi/Many-Objective Particle Swarm Optimization
We propose to couple the R2 performance measure and Particle Swarm Optimization in order to handle multi/many-objective problems. Our proposal shows that through a well-designed interaction process we could maintain the metaheuristic almost inalterable and through the R2 performance measure we did not use neither an external archive nor Pareto dominance to guide the search. The proposed approac...
متن کاملMulti-Objective Optimization of Solar Thermal Energy Storage Using Hybrid of Particle Swarm Optimization and Multiple Crossover and Mutation Operator
Increasing of net energy storage (Q net) and discharge time of phase change material (t PCM), simultaneously, are important purpose in the design of solar systems. In the present paper, Multi-Objective (MO) based on hybrid of Particle Swarm Optimization (PSO) and multiple crossover and mutation operator is used for Pareto based optimization of solar systems. The conflicting objectives are Q net...
متن کاملParticle Swarm Optimization based on Multiple Swarms and Opposition-based Learning*
Standard particle swarm optimization is easy to fall into local optimum and has the problem of low precision. To solve these problems, the paper proposes an effective approach, called particle swarm optimization based on multiple swarms and opposition-based learning, which divides swarm into two subswarms. The 1st sub-swarm employs PSO evolution model in order to hold the self-learning ability;...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Procedia Engineering
سال: 2011
ISSN: 1877-7058
DOI: 10.1016/j.proeng.2011.08.632