Due to their lower productivity, lower reliability, and lower economic stability, older power plants are leading to higher carbon emissions. Rather than simply focusing on the retirement and recuperation of power plants, this study focuses on generation expansion planning (GEP). Considering recuperation is economically and environmentally beneficial to power the power generating company. These criteria have made the GEP problem more complex. Hence, the applications of optimization algorithms are required to solve these complex, constrained, and large-scale problems. In this study, an effective hybrid spotted hyena-particle swarm optimization (HSHPSO) algorithm is proposed to handle the GEP problem for the Tamil Nadu power system. This case study addresses the GEP problem for a 7-year planning horizon (2020–2027), as well as a 14-year planning horizon (2020–2034). A significant reduction in total cost and pollution occurs by including retirement and recuperation in GEP. To prove the effectiveness of the proposed HSHPSO technique, it is compared with the existing technologies such as particle swarm optimization (PSO) and differential evolution (DE). Compared to GEP with no recuperation or retirement, the total cost and CO2 emissions of the GEP have been reduced by 11.07% and 9.48%, respectively. Also, the results demonstrate that the HSHPSO algorithm outperformed other algorithms.