Balancing exploration and exploitation is a common challenge in optimization algorithms, often leading to suboptimal performance. To address this issue, researchers have introduced the spider wasp optimization (SWO) algorithm, inspired by the natural hunting, nesting, and mating behaviors of female spider wasps. A key advantage of this algorithm is its ability to seamlessly between exploration and exploitation, achieving effective results in both processes. The primary focus of the SWO algorithm is to solve the optimal power flow problem in power systems. This involves optimizing several objective functions, including fuel cost, pollutant emissions, active power losses, and voltage standard deviation. For multi-objective optimization, the SWO algorithm is extended using the Pareto concept to form the multi-objective spider wasp optimization (MOSWO) algorithm. The MOSWO algorithm applies fuzzy membership theory to extract the best compromise from non-dominant solutions, generating a well-distributed Pareto front. The performance of the MOSWO algorithm is evaluated on a set of constrained and unconstrained functions and compared against other established algorithms using three widely recognized performance indicators. Additionally, three standard tests (IEEE 30-, as well as IEEE 57- and 118-bus systems) are conducted to verify the efficacy of the SWO and MOSWO algorithms in addressing both single- and multiple-objective functions across 14 studied cases. Numerical results, along with comparisons to state-of-the-art algorithms, demonstrate the superior capability of the proposed algorithms in finding high-quality solutions for single objectives, generating well-distributed Pareto front solutions, and identifying optimal compromises for multi-objective functions.
