Development and application of slime mould algorithm for optimal economic emission dispatch

• Published in: Expert systems with applications 2021-11, Vol.182, p.115205, Article 115205
• Main Authors: Hassan, Mohamed H., Kamel, Salah, Abualigah, Laith, Eid, Ahmad
• Format: Article
• Language: English
• Subjects: Algorithms; Decision making; Economic and emission dispatch; Economics; Emission analysis; Greenhouse gases; ISMA; Particle swarm optimization; Performance enhancement; Slime; Slime mould algorithm; Trigonometric functions; Valve point loading
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2021.115205

•The Improved Slime Mould Algorithm (ISMA) has been derived and proposed.•The economic emission dispatch problem is solved using ISMA and other algorithms.•The proposed ISMA outperforms SMA, PSO, and TSA in solving the EED problem.•Both single- and multi-objective functions are solved using the proposed ISMA. In this paper, an Improved version of the Slime Mould Algorithm (ISMA) is proposed and applied to efficiently solve the single-and bi-objective Economic and Emission Dispatch (EED) problems considering valve point effect. ISMA is developed to improve the performance of the conventional Slime Mould Algorithm (SMA). In ISMA, the solution positions are updated depending on two equations borrowed from the sine–cosine algorithm (SCA) to obtain the best solution. Multi-objective SMA (MOSMA) and Multi-objective ISMA (MOISMA) are developed based on the Pareto dominance concept and fuzzy decision-making. In the multi-objective EED problem, MOSMA and MOISMA are applied to minimize the total fuel costs and total emission with the valve point effect simultaneously. The proposed single-and bi-objective economic emission dispatch algorithms are validated using five test systems, 6-units, 10-units, 11-units, 40-units, and 110-units. The performance of the proposed algorithm is compared with Harris Hawk Optimizer (HHO), JellyfishSearch optimizer (JS), Tunicate Swarm Algorithm (TSA), Particle swarm optimization (PSO), and SMA algorithms. The results show that the proposed algorithms are more robust than other well-known algorithms. Feasible solutions using the proposed algorithms are also achieved, which adjust the schedule of generation without violation of the operating generation limits.