A novel hybrid self-adaptive heuristic algorithm to handle single- and multi-objective optimal power flow problems

The optimal power flow (OPF) is a key tool in the planning and operation of systems, aims to optimize operational costs involved production transport energy by adjusting control variables meet operational, economic, environmental constraints. To achieve this goal, successful implementation an expeditious reliable optimization algorithm crucial. end, paper proposes scrutinizes novel fuzzy adaptive hybrid configuration oriented joint self-adaptive particle swarm (SPSO) differential evolution algorithms, namely FAHSPSO-DE, address multi-objective OPF (MOOPF) problem. For sake practicality, objectives with innate differences such as total fuel cost, active losses, emission are selected. Due practical limitations real additional restrictions, including valve-point effect, multi-fuel characteristic, prohibited operating zones, also taken into account. In order validate performance proposed approach, ten various benchmark functions examined, while three IEEE standard systems 30-, 57-, 118-bus test employed demonstrate suitability approach solving problem expeditiously. Results have been compared those literature show effectiveness our proposal handling different scales, multi-objective, non-convex problems.