In this paper, an Electoral Cooperative Particle Swarm Optimization (ECPSO) based on several subswarms is presented to solving the Permutation Flow Shop Scheduling Problem (PFSSP). In the proposed algorithm, several strategies are employed to avoid falling into local optimum, improve the diversity and achieve better solution. Firstly, a electoral swarm is generated by the voting of primitive su...

This paper presents an algorithm for solving the multi-objective reactive power dispatch problem in power system. Modal analysis of the system is used for static voltage stability assessment. Loss minimization and maximization of voltage stability margin are taken as the objectives. Generator terminal voltages, reactive power generation of the capacitor banks and tap changing transformer settin...

This paper presents a novel explicit exploration information exchange mechanism for niche technique. In this framework, the whole population is divided into many sub-populations. The different sub-population communicates with each other. One sub-population exploration area does not be explored by others. Based on this framework, a multi-sub-swarm particle swarm optimization (MSSPSO) algorithm i...

Particle swarm optimization cannot guarantee convergence to the global optimum on multi-modal functions, so multiple swarms can be useful. One means to coordinate these swarms is to use a separate search mechanism to identify different regions of the solution space for each swarm to explore. The expectation is that these independent subswarms can each perform an effective search around the regi...

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).

High dimensional biomedical datasets contain thousands of features which can be used in molecular diagnosis of disease, however, such datasets contain many irrelevant or weak correlation features which influence the predictive accuracy of diagnosis. Without a feature selection algorithm, it is difficult for the existing classification techniques to accurately identify patterns in the features. ...

In this paper, a BCC-DPSO scheduling algorithm is proposed to solve multi-objective optimization problem for task scheduling on Network-on-Chip (NoC). In our proposal, the relative advantage of the solution is evaluated by calculating its efficiency using BCC model in Data Envelopment Analysis (DEA), and the referred-time method is introduced to rank the BCC-efficient solution. Moreover, a sub-...

Shuffled frog leaping algorithm is a new kind of swarm intelligence optimization algorithm. Due to the local search of the basic shuffled frog leaping algorithm which is the only by the worst frog to search and jump, searching ability of which was limited; therefore it had the low precision, slow convergence speed and easy premature convergence etc. Therefore, in order to enhance the ability of...

Abstract. We study self-organized cooperation in a heterogeneous robotic swarm consisting of two sub-swarms. The robots of each sub-swarm play distinct roles based on their different characteristics. We investigate how the swarm as a whole can solve complex tasks through a self-organized process based on local interactions between the sub-swarms. We focus on an indoor navigation task, in which ...

Multilevel thresholding is an important technique for image processing and pattern recognition. The maximum entropy thresholding (MET) has been widely applied in the literature. In this paper, a new multilevel MET algorithm based on the technology of the shuffled frog-leaping (SFLO) algorithm is proposed: called the maximum entropy based shuffled frog-leaping algorithm thresholding (MESFLOT) me...