An enhanced class topper algorithm based on particle swarm optimizer for global optimization

Class topper optimization (CTO) algorithm divides the initial swarm into several sub-swarms, and this causes it to possess a strong exploration ability throughout optimization. It however randomly selects best-ranked particles as section toppers (ST’s) and class topper (CT), and the inability of every particle to directly learn from the CT causes slow convergence during the latter stages of iterations. To overcome the algorithm’s deficiency and find a good balance between exploration and exploitation, this study proposes an enhanced CTO (ECTPSO) based on the social learning characteristics of particle swarm optimization (PSO). We created an external archive called the assertive repository (AR) to store best-ranked particles and employed the Karush-Kuhn-Tucker (KKT) proximity measure to assist in the selection of STs and CT. Also, the intensive crowded sorting (ICS) is developed to truncate the AR when it exceeds its maximum size limit. To further encourage exploitation and avert particles from getting trapped in local optimum, we incorporated an adaptive performance adjustment strategy (APA) into our framework to activate particles when they are stagnated. The CEC2017 test suite is employed to evaluate the effectiveness of the proposed algorithm and four other benchmark peer algorithms. The results show that our proposed method possesses a better capability to elude local optima with faster convergence than the other peer algorithms. Furthermore, the algorithms were applied to economic load dispatch (ELD), of which our proposed algorithm demonstrated its effectiveness and competitiveness to address optimization problems.