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Summary This paper presents a creature‐inspired stud krill herd (SKH) algorithm consisting of stud selection and crossover operators, which are augmented in the krill herd algorithm to solve the optimal power flow (OPF) problems in a power system. Due to the nonlinear and nonconvex nature of the OPF problem, it is quite difficult to find optimal solution using conventional optimization algorithms. Here in SKH, a stud‐selection operator is introduced in the krill herd algorithm to further enhance the quality of the solution, and crossover operator is embedded to avoid premature convergence. To investigate the performance, the proposed SKH algorithm has been demonstrated on the OPF problems of IEEE 30‐bus, Algerian 59‐bus, and IEEE 118‐bus systems considering various objective functions such as total production cost, L‐index, power loss, and emission pollution to be minimized. The OPF results have been compared with those obtained with other methods, and it has been found that SKH algorithm is successful in providing better quality solutions in reasonable execution times.

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In the present paper, a novel meta-heuristic algorithm, namely quasi-oppositional search-based political optimizer (QOPO), is proposed to solve a non-convex single and bi-objective economic and emission load dispatch problem (EELDP). In the proposed QOPO technique, an opposite estimate candidate solution is performed simultaneously on each candidate solution of the political optimizer to find a better solution of EELDP. In the bi-objective EELDP, QOPSO is applied to simultaneously minimize fuel costs and emissions by considering various constraints such as the valve-point loading effect (VPLE) and generator limits for a generation. The effectiveness of the proposed QOPO technique has been applied on three units, six units, 10-units, 11-units, 13-units, and 40-unit systems by considering the VPLE, transmission line losses, and generator limits. The results obtained using the proposed QOPO are compared with those obtained by other techniques reported in the literature. The relative results divulge that the proposed QOPO technique has a good exploration and exploitation capability to determine the optimal global solution compared to the other methods provided in the literature without violation of any constraints and bounded limits.