Particle Swarm Optimization (PSO) is a metaheuristic optimization algorithm that owes much of its allure to its simplicity and its high effectiveness in solving sophisticated optimization problems. However, since the performance of the standard PSO is prone to being trapped in local extrema, abundant variants of PSO have been proposed by far. For instance, Fuzzy Adaptive PSO (FAPSO) algorithms ...

This paper describes an evolutionary strategy called PSOGA-NN, which uses Neural Network (NN) for selfadaptive control of hybrid Particle Swarm Optimization and Adaptive Plan system with Genetic Algorithm (PSO-APGA) to solve large scale problems and constrained real-parameter optimization. This approach combines the search ability of all optimization techniques (PSO, GA) for stability of conver...

This article introduces two swarm intelligent algorithms, a group search optimizer (GSO) and an artificial fish swarm algorithm (AFSA). A single intelligent algorithm always has both merits in its specific formulation and deficiencies due to its inherent limitations. Therefore, we propose a mixture of these algorithms to create a new hybrid optimization algorithm known as the group search-artif...

This paper presents a hybrid approach for clustering based on particle swarm optimization (PSO) and bacteria foraging algorithms (BFA). The new method AutoCPB (Auto-Clustering based on particle bacterial foraging) makes use of autonomous agents whose primary objective is to cluster chunks of data by using simplistic collaboration. Inspired by the advances in clustering using particle swarm opti...

A novel model including social, environmental and economic benefits is proposed in hybrid thermal/wind power system and studied by Karush-Kuhn-Tucker and hybrid particle swarm optimization techniques. Our work is the first to develop social dispatch model by calculating risk caused by wind power. Then the novel multi-objective optimization model of social-environment-economic dispatch is establ...

In this paper we propose a new distributed double guided hybrid algorithm combining the particle swarm optimization (PSO) with genetic algorithms (GA) to resolve maximal constraint satisfaction problems (Max-CSPs). It consists on a multi-agent approach inspired by a centralized version of hybrid algorithm called Genetical Swarm Optimization (GSO). Our approach consists of a set of evolutionary ...

The power supply of remote sites and applications at minimal cost and with low emissions is an important issue when discussing future energy concepts. This paper presents modeling and optimization of a photovoltaic (PV)/wind/diesel system with batteries storage for electrification to an off-grid remote area located in Rafsanjan, Iran. For this location, different hybrid systems are studied and ...