Solving Combined Economic Emission Dispatch Problems Using Multi-objective Hybrid Evolutionary-Barnacles Mating Optimization

• Published in: Environmental Science and Engineering
• Main Author: Ismail N.L.; Musirin I.; Dahlan N.Y.; Mansor M.H.; Senthil Kumar A.V.
• Format: Conference paper
• Language: English
• Published: Springer Science and Business Media Deutschland GmbH 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85199156651&doi=10.1007%2f978-981-97-0372-2_7&partnerID=40&md5=408a08b3900bc423a8ec40b97b60f674
• ISSN: 18635520
• DOI: 10.1007/978-981-97-0372-2_7

This paper introduces the Multi-objective Optimization Hybrid Evolutionary-Barnacles Mating Optimizer (MOHEBMO) algorithm, developed to solve multiple objectives simultaneously using the weighted sum method. MOHEBMO combines Evolutionary Programming and Barnacles Mating Optimizer to find the best trade-off among conflicting objectives. The algorithm is applied to the IEEE 30 Bus RTS with six generators, aiming to optimize total generation cost and total emission. Two case studies are conducted to evaluate the efficiency of the MOHEBMO, with simulations performed using MATLAB software. The algorithm's performance is compared with existing methods for solving non-convex multi-objective combined economic emission dispatch problems. The results indicate that MOHEBMO outperforms these existing algorithms, demonstrating its capability in determining the lowest optimal solution for both total generation cost and total emission. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.