Impact of Constraint Handling on the Performance of PSO on CSTHTS Problem: An Improvement in Results

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.

[1]  M. S. Fakhar,et al.  Impact of parameter control on the performance of APSO and PSO algorithms for the CSTHTS problem: An improvement in algorithmic structure and results , 2021, PloS one.

[2]  Sanjeevikumar Padmanaban,et al.  Implementation of APSO and Improved APSO on Non-Cascaded and Cascaded Short Term Hydrothermal Scheduling , 2021, IEEE Access.

[3]  Sanjeevikumar Padmanaban,et al.  Conventional and Metaheuristic Optimization Algorithms for Solving Short Term Hydrothermal Scheduling Problem: A Review , 2021, IEEE Access.

[4]  Sanjeevikumar Padmanaban,et al.  Application of Dynamically Search Space Squeezed Modified Firefly Algorithm to a Novel Short Term Economic Dispatch of Multi-Generation Systems , 2021, IEEE Access.

[5]  Omer Saleem,et al.  Comparison of Firefly and Hybrid Firefly-APSO Algorithm for Power Economic Dispatch Problem , 2020, 2020 International Conference on Technology and Policy in Energy and Electric Power (ICT-PEP).

[6]  Intisar Ali Sajjad,et al.  Statistical Performances Evaluation of APSO and Improved APSO for Short Term Hydrothermal Scheduling Problem , 2019, Applied Sciences.

[7]  Najeh Ben Guedria,et al.  Improved accelerated PSO algorithm for mechanical engineering optimization problems , 2016, Appl. Soft Comput..

[8]  Christian Blum,et al.  Hybrid Metaheuristics , 2010, Artificial Intelligence: Foundations, Theory, and Algorithms.

[9]  Provas Kumar Roy,et al.  Teaching learning based optimization for short-term hydrothermal scheduling problem considering valve point effect and prohibited discharge constraint , 2013 .

[10]  Jingrui Zhang,et al.  Small Population-Based Particle Swarm Optimization for Short-Term Hydrothermal Scheduling , 2012, IEEE Transactions on Power Systems.

[11]  Amita Mahor,et al.  Short term generation scheduling of cascaded hydro electric system using novel self adaptive inertia weight PSO , 2012 .

[12]  Niladri Chakraborty,et al.  A New Improved Particle Swarm Optimization Technique for Daily Economic Generation Scheduling of Cascaded Hydrothermal Systems , 2010, SEMCCO.

[13]  Yanbin Yuan,et al.  An improved PSO approach to short-term economic dispatch of cascaded hydropower plants , 2010, Kybernetes.

[14]  Nima Amjady,et al.  Daily Hydrothermal Generation Scheduling by a new Modified Adaptive Particle Swarm Optimization technique , 2010 .

[15]  R. Chakrabarti,et al.  An improved PSO technique for short-term optimal hydrothermal scheduling , 2009 .

[16]  Xiaohui Yuan,et al.  Application of enhanced PSO approach to optimal scheduling of hydro system , 2008 .

[17]  N. Chakraborty,et al.  Differential evolution technique-based short-term economic generation scheduling of hydrothermal systems , 2008 .

[18]  Christian Blum,et al.  Hybrid Metaheuristics, An Emerging Approach to Optimization , 2008, Hybrid Metaheuristics.

[19]  Sushil Kumar,et al.  Efficient real coded genetic algorithm to solve the non-convex hydrothermal scheduling problem , 2007 .

[20]  Xiaohui Yuan,et al.  Short-term hydro-thermal scheduling using particle swarm optimization method , 2007 .

[21]  L. Lakshminarasimman,et al.  Short-term scheduling of hydrothermal power system with cascaded reservoirs by using modified differential evolution , 2006 .

[22]  P. K. Chattopadhyay,et al.  Fast evolutionary programming techniques for short-term hydrothermal scheduling , 2003 .

[23]  Malcolm Irving,et al.  A genetic algorithm modelling framework and solution technique for short term optimal hydrothermal scheduling , 1998 .