Research On Multi-objective Artificial Raindrop Algorithm Driven By Prior Knowledge And Its Applications

Evolutionary algorithms (EAs) are a kind of intelligent search and optimization techniques inspired by natural phenomena or laws. Due to their high efficient optimization performance and huge application potentials, EAs have attracted wide attention by domestic and foreign researchers over the past half century. In response, this thesis aims at the recently proposed EA-artificial raindrop algorithm (ARA), and its ability in solving complex continuous optimization problems. The main contributions are summarized as follows:(1) To further understand the operation mechanism and calculation results of artificial raindrop algorithm, some relevant mathematical theories is firstly used to it prove that ARA can converge to a satisfactory population with probability one. Then, ARA is compared with three state-of-the-art EAs on the CEC2005 test platform. The experimental results have indicated its efficiency in solving complex continuous optimization problems. Fially, similarities and differences of ARA compared with other nature-inspired metaheuristic algorithms including PSO and IWDA are highlight. Three algorithms are categorized as population-based and nature-inspired metaheuristic algorithms, but, there are many apparent differences among them,like searching strategy, information sharing and so on. We can learn from other algorithms and improve the performance of algorithms.(2) When using ARA to solve multi-objective optimization problems (MOPs),the important aspect to improve the search efficiency is how to combine the characteristics of the problem in the algorithm design process. For this reason, an efficient multi-objective artificial raindrop algorithm (MOARA) with prior knowledge is proposed. To improve the exploratory ability, the center point sampling strategy (CPSS) and binomialo crossover operator (BCO) is integrated into MO ARA. The primary role of BCO is to accelerate the filling of the Pareto front(PF) by recombining diverse solutions, whereas CPSS serves as the domain knowledge of MOPs for guiding other points towards the target PF. Furthermore, the flow chart and pseuo code of the MOARA are drawn, according to the description and analysis of the algorithms and the theoretical analysis of the algorithm is made from both computational complexity and convergence proof, it is proved that MOARA can converge to the ideal Pareto optimal set with probability one. For performance evaluation and comparison purposes, the proposed approach has been applied to twelve benchmark MOPs, and compared with four state-of-the-art multi-objective evolutionary algorithms based on non-dominated sorting. The evaluation of the performance of the different in mutil-objective agorithms is inverted generational distance(IGD). The experimental results have indicated that MOARA is closer to the ideal PF than the other four compared algorithms and both techniques have the ability to jump out of the Pareto local optimal values more quickly.in summary MOARA is efficiency over other compared approaches.(3) For the reactive power optimization problem in electric power system, a tri-objective optimization problem is first established based on the voltage deviation, and active power loss.Then, MOARA is applied to the IEEE-30 node system and the coding and flow of the algorithm are described in detail. Last, the experimental results show that the MOARA not only realizes the economic stability of the power system, but also improves the voltage stability of the power grid, MOARA has practical significance.