Short-term hydrothermal scheduling using Time Varying Acceleration coefficient based Particle Swarm Optimization with Constriction Factor and Inertia Weight Approach

Conventional and Metaheuristic Optimization Algorithms for Solving Short Term Hydrothermal Scheduling Problem: A Review

“…Reference [121] combined the PSO and differential evolution algorithm to solve the MOSTHTS problem while adopting the penalty factor approach to combine the two objectives into a single objective. Reference [122] solved CSTHTS problem while considering the valve point loading of thermal units by using PSO algorithm that updates the acceleration coefficients, inertia weight and constriction coefficients iteration to iteration. Reference [123] solved CSTHTS problem using modified dynamic neighbourhood learning-based PSO.…”

“…Table 8 summarizes implementation of PSO and its variants for STHTS. [92], [94], [97], [105], [110], [112], [124], [128], [134] Near With different neighborhood topologies [92], [123], [130] Constriction factor PSO [113], [115], [117], [129] Hybrid of PSO and Evolutionary programming [95] Updating Inertia weights PSO [106] Quantum behaved PSO [98], [99], [127] Modified adaptive PSO [100] Self-organizing hierarchical PSO [101], [102] Time varying acceleration coefficients PSO [103], [122] Improved PSO [96], [104], [109] Efficient PSO [107] Mixed-binary evolutionary PSO [111] Dynamically controlled PSO [114], [125] Hybrid of PSO and DE [116], [121] Hybrid of PSO and direct search method [118] Enhanced PSO [120] Auxiliary search based PSO [131] Hybrid of PSO and GSA [132] Fully informed PSO [135] Accelerated PSO [136], [137] FIGURE 9. Year wise distribution of arti...…”