文章基本信息

标题：Multi-objective Improved Bat Algorithm for Optimizing Fuel Cost, Emission and Active Power Loss in Power System
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作者：Gonggui Chen ; Jie Qian ; Zhizhong Zhang 等
期刊名称：IAENG International Journal of Computer Science
印刷版ISSN：1819-656X
电子版ISSN：1819-9224
出版年度：2019
卷号：46
期号：1
页码：118-133
出版社：IAENG - International Association of Engineers
摘要：In this paper, a new multi-objective improved bat algorithm (MOIBA) is proposed to solve the constrained multi-objective optimal power flow (MOOPF) problem with contradictory objectives. The proposed MOIBA algorithm, introducing nonlinear inertia weight, global optimal guiding mechanism and monotone random filling model based on extreme (MRFE), can improve the shortcomings of basic bat algorithm which is easy to fall into local optimum. A Pareto-dominant method with constraint priority (PMC) is proposed to ensure that state variables can satisfy the inequality constraints of MOOPF problem. To obtain well-distributed Pareto optimal set (POS), an elite non-dominated sorting method with crowding-distance (ESCD) is adopted. In addition, a fuzzy affiliation approach (FAA) is used to select the best compromise (BC) from the obtained POS. The IEEE 30-bus, IEEE 57-bus and IEEE 118-bus systems are employed to evaluate the effectiveness of MOIBA with four objectives, which includes optimizing basic fuel cost and emission concurrently, optimizing basic fuel cost and active power loss concurrently, optimizing fuel cost with value-point loadings and active power loss concurrently, optimizing basic fuel cost, emission and active power loss concurrently. The legion experimental results obtained by MOIBA, which are contrast to MOPSO and MODE algorithms, validate that MOIBA has definite competitive advantages to achieve satisfactory POS. Furthermore, two performance metrics, generational distance (GD) and spacing (SP), are chosen to estimate the distribution and diversity of Pareto solutions obtained by MOIBA.
关键词：Multi;objective Improved Bat Algorithm (MOIBA); Multi;objective Optimal Power Flow (MOOPF); Elite non;dominated sorting approach with crowding;distance (ESCD); generational distance (GD); spacing (SP);