## 1 Introduction  
Currently, real-life problems share a common aspect, which have multiple objectives to be optimized. Problems, such as recommender systems [1], industry [2] and bioinformatics [3] have many objectives that need to be optimized simultaneously to reach an effective solution. These objectives are often contradictory, so optimizing one objective deteriorates the degree of optimization of other objectives. Solving Multi-Objective Problems (_MOPs_) needs multiple solutions to vary the degree of optimization of each objective [4]. Choosing the appropriate solution can be specified by a decision-maker. Population-based meta-heuristic methods mimic the intelligent behavior of organisms that live in groups [5]. They use a set of solutions to cover solution space, which makes them suitable for solving _MOPs_. Population-based meta-heuristic methods can be simply classified into Swarm Intelligence (_SI_) and Evolutionary Algorithmic (_EA_) methods [6]. _SI_ methods have been first proposedto solve _MOPs_ by [PERSON] [7]. They represent each solution to the _MOP_ by a swarm member. They use intelligent intensification and diversification techniques to cover solution space economically. The quality of solutions is assessed using objective functions of the _MOP_. To identify best solutions regarding multiple objectives, a sophisticated method is needed. Pareto Optimization (_PO_) has been one of the main techniques used in solving _MOPs_[8]. It aims to optimize the whole objectives simultaneously. _PO_ generates a set of best solutions called \"non-dominated\" solutions or \"Pareto Front\" (_PF_) [8]. These solutions represent the best solutions to the problem, which cannot be further optimized. Many population-based methods have been combined with _PO_ to solve _MOPs_, see for example Non-dominated Sorting Genetic Algorithm (_NSGA_) [9], _NSGA-II_[10], Multi-Objective Particle Swarm Optimization (_MOPSO_) [11], Multi-Objective Cat Swarm Optimization (_MOCSO_) [12], and Multi-Objective Grey Wolf Optimizer (_MOGW_) [13], to name a few. Yet although _SI_ methods presented a good performance in solving _MOPs_, they have their own drawbacks. Focusing on methods, such as Particle Swarm Optimization (_PSO_) and its rivals, these methods share common drawbacks such as selecting the swarm leader [14], getting trapped in local optima [15], and obtaining solutions that are not evenly distributed over solution space [15]. These problems result from the lack of mature memory elements, since only a single best solution within the search space is kept, which limits their capabilities to cover the whole solution space. Even when those methods use an external archive to store best solutions, they do not use these solutions to guide swarm members. This limits their ability to search promising regions thoroughly and increases their probability to get trapped in local optima. Also, _PSO_ and its rivals tend to move with big steps to explore different regions of the search space without focusing on exploring neighbors of swarm members, which increases their need to local search methods. Combining _PSO_ or one of its rivals with mature memory elements can enhance its effectiveness. Tabu Search (_TS_) is a well-known single-point meta-heuristic method, proposed by [PERSON] [16]. In _TS_, an Elite List (_EL_) is used to store the best solutions, and a Tabu List (_TL_) is used to store pre-visited solutions. These memory elements enable _TS_ to identify promising regions to be searched thoroughly and remembering pre-visited regions to be avoided. While Optimization Algorithm (_WOA_) is a recent _SI_ method, proposed by [PERSON] et al. [17]. It mimicked the behavior of the humpback whales to heuristically locate the extreme points of arbitrary functions. _WOA_ was tested over multiple engineering optimization problems and presented a good performance [17]. Also, it was successfully used in solving the scheduling problem of a single robot used to transport raw materials within an intelligent manufacturing system [18].