Particle Swarm Optimization (PSO) is a brilliant swarm intelligence-based algorithm invented by Kennedy and Eberhart in 1995. Since its birth, it has been applied to many optimization problems to provide robust solutions in several engineering and applied sciences domains. The Cascaded Short Term Hydrothermal Scheduling (CSTHTS) problem, a highly non-linear, non-convex and multimodal scheduling problem, is solved by many metaheuristic algorithms, including PSO in the literature. However, It is found that the results reported on a standard benchmark test case of the CSTHTS problem are very poor compared to the results achieved on the same test case by implementing other advanced metaheuristic optimization algorithms. Therefore, It has been investigated and validated through the implementation that there can be an immense improvement in the performance of the PSO algorithm by just using another better constraint-handling approach. This article presents the improvement in the results of PSO on the CSTHTS problem as given in the literature by implementing a better constraint handling technique.

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