Aiming at robot path planning in an environment with danger sources, a global path planning approach based on multi-objective particle swarm optimization is presented in this paper. First, based on the environment map of a mobile robot described with a series of horizontal and vertical lines, an optimization model of the above problem including two indices, i.e. the length and the danger degree of a path, is established. Then, an improved multi-objective particle swarm optimization algorithm of solving the above model is developed. In this algorithm, a self-adaptive mutation operation based on the degree of a path blocked by obstacles is designed to improve the feasibility of a new path. To improve the performance of our algorithm in exploration, another archive is adopted to save infeasible solutions besides a feasible solutions archive, and the global leader of particles is selected from either the feasible solutions archive or the infeasible one. Moreover, a constrained Pareto domination based on the degree of a path blocked by obstacles is employed to update local leaders of a particle and the two archives. Finally, simulation results confirm the effectiveness of our algorithm.

Multi-objective Particle Swarm Optimization for Robot Path Planning in Environment with Danger Sources

Optimal reactive power planning in power transmission network using sensitivity based bi-level strategy

In a competitive electricity power market, reactive power planning (RPP) is a pivotal matter for the power system researchers from operational and economical view points. RPP is concerned w...

Optimal Reactive Power Management Problem: A Solution Using Evolutionary Algorithms

The optimal coverage problem plays an important role in wireless sensor networks. In this paper a new swarm intelligent technique, Social Emotional Optimisation Algorithm (SEOA), is used to solve the problem based on the probability measuring model. In SEOA, each individual represents a virtual person, and selects the behaviour according to the corresponding emotion index in each iteration. Simulation results showed that the effective coverage has reached 99.98%.

Optimal coverage configuration with social emotional optimisation algorithm in wireless sensor networks

This paper presents a novel stochastic optimization approach to solve the constrained economic load dispatch (ELD) problem using the harmonic search algorithm (HS). HS is a recently developed derivative-free, meta-heuristic optimization algorithm, which draws inspiration from the musical process of searching for a perfect state of harmony. This work analyses the evolution of the population-variance over successive generations in HS and thereby draws some important conclusions regarding the explorative power of HS. The proposed methodology easily takes care of solving non-convex ELD problems along with different constraints like transmission losses, ramp rate limits of the generators and prohibited operating zones. Simulations were performed over various standard test systems with different number of generating units and a comparative study is carried out with other existing relevant approaches. The findings affirmed the robustness and proficiency of the proposed methodology over other existing techniques.

Economic load dispatch using population-variance harmony search algorithm

This paper involves the application of particle swarm optimisation in the optimisation problem of reactive power. The optimisation model of reactive power is first introduced and the improved strategy of particle swarm optimisation is proposed for the problem of optimisation of reactive power in this paper. In order to improve the local search ability, the disturbance item is given for the updating equation of the particle in the improved strategy. The numerical examples of standard IEEE-6 and IEEE-30 power systems for the improved strategy of particle swarm optimisation are performed for the reactive power optimisation. The effectiveness of the improved strategy proposed in this paper has been demonstrated preliminarily from the numerical examples.

Improved strategy of particle swarm optimisation algorithm for reactive power optimisation

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Improved strategy of particle swarm optimisation algorithm for reactive power optimisation