The fast convergence of particle swarm algorithms can become a downside in multi-objective optimization problems when there are many local optimal fronts. In such a situation a multi-objective particle swarm algorithm may get stuck to a local Pareto optimal front. In this paper we propose a new approach in selecting leaders for the particles to follow, which in-turn will guide the algorithm tow...

In this paper, we present a new algorithm of swarm intelligence, namely, Cat Swarm Optimization (CSO). CSO is generated by observing the behaviors of cats, and composed of two sub-models, i.e., tracing mode and seeking mode, which model upon the behaviors of cats. Experimental results using six test functions demonstrate that CSO has much better performance than Particle Swarm Optimization (PSO).

The loss of population diversity is one of main reasons which lead standard particle swarm optimization (SPSO) to suffer from the premature convergence when solving complex multimodal problems. In SPSO, the personal experience and sharing experience are processed with a completely random strategy. It is still an unsolved problem whether the completely random processing strategy is good for main...

The purpose of this paper is to present the extended version of the conventional DEED to overcome the ramp rate violations when its optimal solutions for one period (normally one day) are implemented repeatedly and periodically over consequent dispatch periods to meet the periodic load demands. This dynamic dispatch problem, which is referred to as EDEED, is a multi-objective optimization probl...