An improved Manta ray foraging optimizer for cost-effective emission dispatch problems. (April 2021) - Hassan, Mohamed H.; Houssein, Essam H.; Mahdy, Mohamed A.; Kamel, Salah
Abstract: Recently, Manta ray foraging optimization (MRFO) has been developed and applied for solving few engineering optimization problems. In this paper, an elegant approach based on MRFO integrated with Gradient-Based Optimizer (GBO), named MRFO–GBO, is proposed to efficiently solve the economic emission dispatch (EED) problems. The proposed MRFO–GBO aims to reduce the probability of original MRFO to get trapped into local optima as well as accelerate the solution process. The goal of solving optimal Economic Emission Dispatch (EED) is to economically provide all required electrical loads as well as minimizing the emission with satisfying the operating equality and inequality constraints. Single and multi-objective EED problems are solved using the proposed MRFO–GBO and classical MRFO. In multi-objective EED, fuzzy set theory is adapted to determine the best compromise solution among Pareto optimal solutions. The proposed algorithm is firstly validated through well-known CEC'17 test functions, and then applied for solving several scenarios of EED problems for three electrical systems with 3-generators, 5-generators, and 6-generators. The validation is achieved through different load levels of the tested systems to prove the robustness of the proposed algorithm. The results obtained by the proposed MRFO–GBO are compared with those obtained by recently published optimization techniques as well as the original MRFO and GBO. The results illustrate the ability of the proposed MRFO–GBO in effectively solving the single and multi-objective EED problems in terms of precision, robustness, and convergence characteristics. Highlights: Enhanced Manta ray foraging optimization is proposed called MRFO–GBO. The performance of proposed algorithm is verified on CEC'17 test suite. MRFO–GBO is effectively applied to solve single and multi-objective EED problems. MRFO–GBO is comprehensively compared with other well-know algorithms.