Image entropy thresholding approach has drawn the attentions in image segmatation. The endeavor of this paper is focused on multilevel thresholding using the minimum cross enrtop criterion. In the literature, the particle swarm optimization (PSO) had been applied to conducting the thresold selection. The adopted algorithm used in this paper is the honey bee mating optimization (HBMO). In experi...

Particle Swarm Optimization (PSO) is a bioinspired meta-heuristic for solving complex global optimization problems. In standard PSO, the particle swarm frequently gets attracted by suboptimal solutions, causing premature convergence of the algorithm and swarm stagnation. Once the particles have been attracted to a local optimum, they continue the search process within a minuscule region of the ...

This paper presents a Hybrid Particle Swarm Optimization (HPSO) method for solving the Task Assignment Problem (TAP) which is an np-hard problem. Particle Swarm Optimization (PSO) is a recently developed population based heuristic optimization technique. The algorithm has been developed to dynamically schedule heterogeneous tasks on to heterogeneous processors in a distributed setup. Load balan...

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...

Particle swarm optimization algorithm is easy to reach premature convergence in the solution process, and fall into the local optimal solution. Aiming at the problem, this paper proposes a particle swarm optimization algorithm with chaotic mapping (CM-PSO). The algorithms uses chaotic mapping function to optimize the initial state of population, improve the probability of obtain optimal solutio...

The Particle Swarm Optimization (PSO) was used to select the three best inputs to explain the input-output relationship of both 'defects' and 'time' models. A ranking-based system was used to select the best features. Using this system, the value of each particle in the swarm represents the importance of each feature. During optimization, the three best-ranked features were used to train the Mu...

Particle swarm optimization (PSO) is a kind of evolutionary algorithm to find optimal solutions for continuous optimization problems. Updating kinetic equations for particle swarm optimization algorithm are improved to solve traveling salesman problem (TSP) based on problem characteristics and discrete variable. Those strategies which are named heuristic factor, reversion mutant and adaptive no...

Keywords: PSO QPSO Mean best position Weight parameter WQPSO a b s t r a c t Quantum-behaved particle swarm optimization (QPSO) algorithm is a global convergence guaranteed algorithms, which outperforms original PSO in search ability but has fewer parameters to control. In this paper, we propose an improved quantum-behaved particle swarm optimization with weighted mean best position according t...

This paper presents a modification of the particle swarm optimization algorithm (PSO) intended to combat the problem of premature convergence observed in many applications of PSO. In the new algorithm, each particle is attracted towards the best previous positions visited by its neighbors, in addition to the other aspects of particle dynamics in PSO. This is accomplished by using the ratio of t...

Based on the life cycle cost (LCC) approach, this paper presents an integral mathematical model and particle swarm optimization (PSO) algorithm for the heating system planning (HSP) problem. The proposed mathematical model minimizes the cost of heating system as the objective for a given life cycle time. For the particularity of HSP problem, the general particle swarm optimization algorithm was...