Chaotic Slime Mould Inspired Hybrid Optimizer Approach for Day Ahead Generation Scheduling Problem

Abstract

Abstract In the world of optimization algorithms, hybrid algorithms are gaining more and more popularity, in this paper, novel hybrid algorithm is proposed to solve generation scheduling problem by using Chaotic Slime mould optimization algorithm (CSMA) and seagull optimization algorithm (SOA). Generation scheduling problem is one of the most challenging problem of modern power system due to its combinatorial nature and non-linear constraints. The proposed optimizer is initially tested for various CEC2017 benchmark problems for its effective analysis and in the next stage, the proposed optimizer has been applied to solve generation scheduling problem. The proposed method works in three steps: first, the CSMA-SOA solves the unit commitment problem while ignoring the various constraints. Second, previous solutions are forced to agree with unavoidable constraints using a heuristic constraints repair mechanism; finally, the proposed method is used to establish the most cost-effective solution in giving timeframe. This study further investigate solar energy. Solar irradiance is thought to have a stochastic nature and adhere to Beta PDF. The performance of hybrid optimizers has been tested on a wide range of units. The results clearly show that the proposed optimizer performs much better than other well-known heuristics, meta-heuristics and hybrid optimizers.