Abstract
In this paper, an endeavor is made to enhance the convergence speed of the recently proposed Rao algorithms. The new upgraded versions of Rao algorithms named as “quasi-oppositional-based Rao algorithms” are proposed in this paper. The quasi-oppositional-based learning is incorporated in the basic Rao algorithms to diversify the searching process of the algorithms. The performance of the proposed algorithms is tested on 51 unconstrained benchmark functions. Also, three multi-objective optimization case studies of different heat sinks such as a single-layered microchannel heat sink (SL-MCHS), a double-layered microchannel heat sink (DL-MCHS), and a plate-fin heat sink (PFHS) are attempted to investigate the effectiveness of the proposed algorithms in solving real-world complex engineering optimization problems. The results obtained using the proposed algorithms are compared with the results obtained using the well-known advanced optimization algorithms such as genetic algorithm (GA), artificial bee colony (ABC), differential evolution (DE), particle swarm optimization (PSO), teaching-learning-based algorithm (TLBO), Jaya algorithm, multi-objective genetic algorithm (MOGA), non-dominated sorting genetic algorithm (NSGA-II), real-coded GA (RCGA), direction-based GA, self-adaptive multi-population (SAMP) Rao algorithms, and basic Rao algorithms. The proposed quasi-oppositional-based Rao algorithms are found superior or competitive to the other optimization algorithms considered.
Quasi-oppositional-based Rao algorithms for multi-objective design optimization of selected heat sinks
