The particle swarm algorithm contains elements which map fairly strongly to the foraging problem in behavioural ecology. In this paper, we show how some simple adaptions to the standard algorithm can make it well suited for the foraging problem. We propose two approaches to model foraging behaviour: the first uses a standard particle swarm algorithm, with the particles just slowing down in the ...

This paper presents a multi-objective Particle Swarm Optimization (PSO) algorithm for worker assignment and mixed-model assembly line balancing problem when task times depend on the worker’s skill level. The objectives of this model are minimization of the number of stations (equivalent to the maximization of the weighted line efficiency), minimization of the weighted smoothness index and minim...

Artificial Bee Colony (ABC) algorithm is one of the most recently introduced swarm-based algorithms. ABC simulates the intelligent foraging behaviour of a honeybee swarm. In this work, ABC is used for optimizing a large set of numerical test functions and the results produced by ABC algorithm are compared with the results obtained by genetic algorithm, particle swarm optimization algorithm, dif...

A potential-based genetic algorithm is proposed for the motion planning of robot swarms. The proposed algorithm consists of a global path planner and a motion planner. The global path planning algorithm plans a trajectory, which the robot swarm should follow, within a Voronoi diagram of the free space. The motion planning algorithm is a genetic algorithm based on artificial potential models. Th...

Optimization algorithms are proposed to tackle different complex problems in different areas. In this paper, we firstly put forward a new memetic evolutionary algorithm, named Monkey King Evolutionary (MKE) Algorithm, for global optimization. Then we make a deep analysis of three update schemes for the proposed algorithm. Finally we give an application of this algorithm to solve least gasoline ...

Swarm Intelligence is the global intelligent behaviour emerged from the interaction of groups of simple agents. The existing swarm intelligence research mainly refers to swarm intelligence optimization, which with ant colony optimization and particle swarm optimization as a representative. And the relevant research work focuses on the performance improvements of the optimization algorithm, whic...

Presented a new hybrid particle swarm algorithm based on P systems, through analyzing the working principle and improved strategy of the elementary particle swarm algorithm. Used the particles algorithm combined with the membrane to form a community, particles use wheel-type structure to communicate the current best particle within the community. The best particles, as Representative, compete f...

By taking advantage of niche sharing scheme,we propose a novel co-evolutionary particle swarm optimization algorithm (NCPSO) to solve permutation flow shop scheduling problem. As the core of this algorithm, niche sharing scheme maximizes the diversity of population and hence improves the quality of individuals. To evaluate the performance of the proposed algorithm, we have use eight Taillard in...

A niche chaotic mutation particle swarm optimization (NCPSO) algorithm is proposed to overcome the problem of loss details of images, the contrast is not obvious and poor adaptability in traditional image enhancement methods. In this algorithm, niching methods and elimination strategy are introduced to improve the global optimization ability. Mutative scale chaos mutation algorithm has refined ...