A Hybrid Formulation between Differential Evolution and Simulated Annealing Algorithms for Optimal Reactive Power Dispatch

Abstract

The aim of this paper is to solve the optimal reactive power dispatch (ORPD) prob lem. Metaheuristic algorithms have b een extensively used to solve optimization problems in a reasonab le time without requiring in-depth knowledge of the treated prob lem. The perform ance of a metaheuristic requires a compromise b etween exploitation and exploration of the search space. However, it is rarely to have the two characteristics in the same search method, where the current emergence of hyb rid methods. This paper presents a hyb rid formulation b etween two different metaheuristics: differential evolution (b ased on a population of solution) and simulated annealing (b ased on a unique solution) to solve ORPD. The first one is characterized with the high capacity of exploration, whil e the second has a good exploitation of the search space. For the control variab les, a mixed representation (continuous/discrete), is proposed. The rob ustness of the method is tested on the IEEE 30 b us test system.

A hybrid bacterial foraging-particle swarm optimization technique for solving optimal reactive power dispatch problem

“…Compute pbest and gbest. Update velocity and position of each bacteria (10). end for j sort bacteria according to the fitness remove the worst half and replace with best half end for k replace certain bacteria with new ones with the probability of Ped end for l printing of the results.…”

“…The concept of hybrid algorithm [8][9][10][11] is introduced to effectively use the advantages of the two algorithms and also to overcome their disadvantages. In BFA, during the process of chemotaxis, it depends on random search which may delay in obtaining global solution.…”

Multi-Area and Multi-Period Optimal Reactive Power Dispatch in Electric Power Systems

“…There are also algorithms inspired by physical phenomena such as the effect of gravity [7], where each possible solution is simulated as an object with a defined mass value, which is used to indicate the fitness of an objective function. There are also algorithms based on the cooling of materials [8], where the temperature changes of a specific material are used to determine an objective function while considering the nonlinearity in the material's temperature changes. Other algorithms are inspired by the behavior of certain groups or organisms, such as particle swarm optimization (PSO) [9,10], ant colony optimization [11][12][13], and artificial bee colony optimization [14,15].…”