Pareto method is widely deployed in solving multi-objective optimization problems which have contradictory objectives. Different from scalarization method, this method is based on the concept of dominance to identify dominated and non-dominated solutions. A solution S can be classified into non-dominated Pareto solution when an objective function cannot be further improved without reducing the objective function of the other. A set of Pareto solutions forms a Pareto front and an insight into trade-off between multiple objectives can be established . Multi-objective optimization algorithms can be cauterised into two main classes, namely, evolutionary and swarm-based algorithms . Evolutionary algorithms are inspired by the concept of natural evolution and commonly deployed methods include Multi-objective Genetic Algorithm (MOGA) , Pareto Archived Evolution Strategy (PAES) , Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) , and Non-Dominated Sorting Genetic Algorithm-III (NSGA-III) . Swarm-based algorithms are inspired by the self-organized and decentralized biological systems and commonly deployed methods include Multi-objective Particle Swarm Optimization (MOPSO) , Multi-objective Ant Colony Optimization (MOACO) and Multi-objective Grey Wolf Optimization (MOGWO) .