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Abstract The Optimal Power Flow (OPF) is a primary tool in planning and installing power systems. It attempts to minimize the operating costs associated with generating and transmitting electrical power by modifying control parameters to satisfy environmental, economic, and operational constraints. Implementing an efficient and robust optimization algorithm for the above‐said problem is critical to achieving such a typical objective. Therefore, this paper introduces and evaluates new variants of the Successive History‐based Adaptive Differential Evolutionary (SHADE) algorithm called ESHADE, SHADE‐SFS, and SHADE‐SAP to solve the OPF problems with equality and inequality constraints. Generally, the static penalty approach is widely used for eliminating infeasible solutions discovered during the search phase when searching for feasible solutions. This approach requires the accurate selection of penalty coefficients, accomplished through the trial‐and‐error method. The proposed ESHADE algorithm is formulated using Self‐Adaptive Penalty (SAP) and Superiority of Feasible Solution (SFS) mechanisms to obtain feasible solutions for OPF problems. Two IEEE bus systems are used to demonstrate the effectiveness of the proposed algorithm in handling OPF problems. The fuel cost and active power loss obtained by the proposed algorithm are better than other state‐of‐the‐art algorithms. The results reveal that the proposed framework offers significant advantages over other algorithms.

The Optimal Power Flow (OPF) is a primary tool in planning and installing power systems. It attempts to minimize the operating costs associated with generating and transmitting electrical power by modifying control parameters to satisfy environmental, economic, and operational constraints. Implementing an efficient and robust optimization algorithm for the above‐said problem is critical to achieving such a typical objective. Therefore, this paper introduces and evaluates new variants of the Successive History‐based Adaptive Differential Evolutionary (SHADE) algorithm called ESHADE, SHADE‐SFS, and SHADE‐SAP to solve the OPF problems with equality and inequality constraints. Generally, the static penalty approach is widely used for eliminating infeasible solutions discovered during the search phase when searching for feasible solutions. This approach requires the accurate selection of penalty coefficients, accomplished through the trial‐and‐error method. The proposed ESHADE algorithm is formulated using Self‐Adaptive Penalty (SAP) and Superiority of Feasible Solution (SFS) mechanisms to obtain feasible solutions for OPF problems. Two IEEE bus systems are used to demonstrate the effectiveness of the proposed algorithm in handling OPF problems. The fuel cost and active power loss obtained by the proposed algorithm are better than other state‐of‐the‐art algorithms. The results reveal that the proposed framework offers significant advantages over other algorithms. [ABSTRACT FROM AUTHOR]

This paper presents a new metaheuristic approach based on a self-adaptive genetic algorithm (SAGA) for solving the short-term hydro-thermal-solar scheduling with combined-cycle (CCGT) units. First of all, the proposed approach is applied to a test system with different characteristics, considering the valve-point effect. The simulation results obtained from the new SAGA are compared with the results obtained from some other metaheuristic methods, such as AIS, DE, and EP to reveal the validity and verify the feasibility of the proposed approach. The test results show that the proposed metaheuristic approach proves the effectiveness and superiority of the SAGA algorithm for solving the short-term hydro-thermal-solar scheduling (SHTSS) problem. [ABSTRACT FROM AUTHOR]

This paper presents a new metaheuristic approach based on a self-adaptive genetic algorithm (SAGA) for solving the short-term hydro-thermal-solar scheduling with combined-cycle (CCGT) units. First of all, the proposed approach is applied to a test system with different characteristics, considering the valve-point effect. The simulation results obtained from the new SAGA are compared with the results obtained from some other metaheuristic methods, such as AIS, DE, and EP to reveal the validity and verify the feasibility of the proposed approach. The test results show that the proposed metaheuristic approach proves the effectiveness and superiority of the SAGA algorithm for solving the short-term hydro-thermal-solar scheduling (SHTSS) problem.

This paper proposes a self-adaptive penalty function and presents a penalty-based algorithm for solving nonsmooth and nonconvex constrained optimization problems. We prove that the general constrained optimization problem is equivalent to a bound constrained problem in the sense that they have the same global solutions. The global minimizer of the penalty function subject to a set of bound constraints may be obtained by a population-based meta-heuristic. Further, a hybrid self-adaptive penalty firefly algorithm, with a local intensification search, is designed, and its convergence analysis is established. The numerical experiments and a comparison with other penalty-based approaches show the effectiveness of the new self-adaptive penalty algorithm in solving constrained global optimization problems. [ABSTRACT FROM AUTHOR]

This paper proposes a novel fuzzy rule-based penalty function approach for solving single-objective nonlinearly constrained optimization problems. Of all the existing state-of-the-art constraint handling techniques, the conventional method of penalty can be easily implemented because of its simplicity but suffers from the lack of robustness. To mitigate the problem of parameter dependency, several forms of adaptive penalties have been suggested in literature. Instead of identifying a complex mathematical function to compute the penalty for constraint violation, we propose a Mamdani type IF-THEN rule-based fuzzy inference system that incorporates all the required criteria of self-adaptive penalty without formulating an explicit mapping. Effectiveness of the proposed constrained optimization algorithm has been empirically validated on the basis of the standard optimality theorems from the literature on mathematical programming. Simulation results show that fuzzy penalty not only surpasses its existing counterpart i.e., self adaptive penalty, but also remain competitive against several other standard as well as currently developed complex constraint handling strategies. [ABSTRACT FROM PUBLISHER]