Particle Swarm Optimization (PSO) is a new paradigm of Swarm Intelligence. Particle swarms are a valuable tool to nd optima in a tness landscape in <n, especially useful when dealing with a high number of dimensions and problems where problem speci c information is non-existent. Its rapid convergence and small computational requirements make it a good candidate for solving optimization problems...

Wireless sensor networks (WSNs) consist of sensor nodes. These networks have huge application in habitat monitoring, disaster management, security and military, etc. Wireless sensor nodes are very small in size and have limited processing capability very low battery power. This restriction of low battery power makes the sensor network prone to failure. Data aggregation is very crucial technique...

Agent Swarm Optimization (ASO) is a generalization of Particle Swarm Optimization (PSO) orientated towards distributed artificial intelligence, taking as a base the concept of multi-agent systems. It is aimed at supporting decision-making processes by solving either single or multi-objective optimization problems. ASO offers a common framework for the plurality of co-existent population-based a...

despite the growing use of evolutionary multi-objective optimization algorithms in different categories of science, these algorithms as a powerful tool in portfolio optimization and specially solving multi-objective portfolio optimization problem is still in its early stages. in this paper, moeas have been used for solving multi-objective portfolio optimization problem in tehran stock market. f...

The rest of this paper is organized as follows. Section 2 describes traditional System Identification and introduces the use of Particle Swarm Optimization (PSO) for determining the coefficients of a simple autoregressive moving average model (SwARMA). Section 3 explains Particle Swarm Optimization. Section 4 describes the results of using PSO for determining the ARMA model parameter (SwARMA) f...

Marriage in Honey Bees Optimization (MBO) is a new swarm-intelligence method, but existing researches concentrate more on its application in single-objective optimization. In this paper, we focus on improving the algorithm to solve the multi-objective problem and increasing its convergence speed. The proposed algorithm is named as multi-objective Particle Swarm Marriage in Honey Bees Optimizati...

This paper mainly discusses the application of the particle swarm optimization in logistics distribution routing problems. Combining with the characteristics of logistics and distribution, it established a mathematical model of the distribution routing problem. Introducing three kinds of optimization strategies in the particle swarm optimization to optimize the particle swarm algorithm, constru...

Particle swarm optimisation (PSO) was born just over ten years ago. The initial ideas on particle swarms of Kennedy and Eberhart were aimed at producing computational intelligence by exploiting simple analogues of social interaction, rather than purely individual cognitive abilities. The first simulations [1] were influenced by Heppner’s and Grenander’s work [2] and involved analogues of bird f...