This paper presents multimodal function optimization, using a nature-inspired glowworm swarm optimization (GSO) algorithm, with applications to collective robotics. GSO is similar to ACO and PSO but with important differences. A key feature of the algorithm is the use of an adaptive local-decision domain, which is used effectively to detect the multiple optimum locations of the multimodal funct...

In this paper we address the problem of multiple odor source localization using mobile robot swarms modelled as network robot systems. These networked robots are used to locate multiple radiation/odor sources like oil spills, leaks in pressurized systems, hazardous plumes/aerosols resulting from nuclear/chemical spills, deepsea hydrothermal vent plumes, fire-origins in forest fires, and hazardo...

The synchronous reluctance motor (Sync-RM) is known for its higher reliability, faster dynamic response, efficiency, speed range, power density, and torque per ampere. Consequently, the scope of research into emerging Sync-RMs drive system has been extended. For different kinds mechanical electrical applications, motors are important components any machine or tool. There several types available...

so far various methods for optimization presented and one of most popular of them are optimization algorithms based on swarm intelligence and also one of most successful of them is particle swarm optimization (pso). prior some efforts by applying fuzzy logic for improving defects of pso such as trapping in local optimums and early convergence has been done. moreover to overcome the problem of i...

blind source separation technique separates mixed signals blindly without any information on the mixing system. in this paper, we have used two evolutionary algorithms, namely, genetic algorithm and particle swarm optimization for blind source separation. in these techniques a novel fitness function that is based on the mutual information and high order statistics is proposed. in order to evalu...

this paper proposes a method to solve multi-objective problems using improved particle swarm optimization. we propose leader particles which guide other particles inside the problem domain. two techniques are suggested for selection and deletion of such particles to improve the optimal solutions. the first one is based on the mean of the m optimal particles and the second one is based on appoin...