Optimizing the multi-objective bidding strategy using min–max technique and modified water wave optimization method

• Published in: Neural computing & applications 2019-09, Vol.31 (9), p.5207-5225
• Main Authors: Azadi Hematabadi, Ahmad, Akbari Foroud, Asghar
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial Intelligence; Computational Biology/Bioinformatics; Computational Science and Engineering; Computer Science; Data Mining and Knowledge Discovery; Image Processing and Computer Vision; Multiple objective analysis; Operators (mathematics); Optimization; Original Article; Performance enhancement; Probability and Statistics in Computer Science; Water waves
• ISSN: 0941-0643; 1433-3058
• DOI: 10.1007/s00521-018-3361-0

This paper presents a new approach for solving the multi-objective optimal bidding strategy problem by integrating the min–max method and water wave optimization (WWO) algorithm for generation companies (GenCos) participating in the linear supply function based electricity market. In the proposed approach, the multi-objective problem is firstly transformed to a single-objective problem using min–max method. Then, this single optimization problem is solved using the chaotic modified WWO algorithm (CMWWO). Two techniques are implemented to improve the performance of the basic WWO. The Bare-bones technique implemented in the refraction operator is modified to increase the search space called modified WWO, and a chaotic map is used instead of rand functions in the WWO’s operators to enhance the convergence speed of algorithm so-called chaotic WWO. To assess the performance of the CMWWO, it is implemented on the IEEE 30-bus system. The results compared with other well-known optimization algorithms including PSO and GA reveal the outstanding performance of the approach.