i am trying to do a research paper, here is my literature review i make when i do my proposal. can you improve my proposal literature review so that i can put it into my reseach paper?

Evolutionary multi-objective optimization has become a popular and useful field of research and application. Evolutionary optimization (EO) algorithms use a population-based approach in which more than one solution participates in an iteration and evolves a new population of solutions in each iteration. The reasons for their popularity are many: (i) EOs do not require any derivative information (ii) EOs are relatively simple to implement and (iii) EOs are exible and have a wide-spread applicability. For solving single-objective optimization problems, particularly in finding a single optimal solution, the use of a population of solutions may sound redundant but in solving multi-objective optimization problems, an EO procedure is a perfect choice. The multi-objective optimization problems, by nature, give rise to a set of Pareto-optimal solutions which need a further processing to arrive at a single preferred solution. To achieve the first task, it becomes quite a natural proposition to use an EO, because the use of population in an iteration helps an EO to simultaneously find multiple non-dominated solutions, which portrays a trade-off among objectives, in a single simulation run. There are many algorithms in the literature for solving multi-objective algorithms. For the GA, the most well regarded multi-objective counterpart is Non-dominated Sorting Genetic Algorithm (NSGA) [5]. Other popular algorithms are: Multi-Objective Particle Swarm Optimization (MOPSO) [6, 7], Multi-Objective Differential Evolution[8, 9], Multi-objective-Ant Colony Optimization [10], Multi objective Grasshopper [11] and many more. All these algorithms are proved to be effective in finding non-dominated solutions for multi-objective problems. In related works on multi objectives with recent applications can be seen in [12] where an improved whale optimization algorithm (WOA) that combine the differential evolution (DE) algorithm and the opposition-based learning (OBL) called MWDEO has been proposed for solving multi objective benchmarks functions. In [13], a multi-objective Artificial Bee Colony Algorithm for optimal adaptive robust controller design has been proposed, while the multi-objective Sine Cosine Algorithm (MOSCA) has been applied in sequential clustering and classification [14]. However, the question is whether we still need more algorithms? According the No Free Lunch theorem for optimization [1], there is no algorithm capable of solving optimization algorithms of all kinds. This theorem logically proved this and allows the proposal of new algorithms or improvement of current ones. Therefore, there is still room for proposing new or improved algorithms so that the researchers have various choice of optimizer to solve more complex problems. This work explores the multi-objective EMA (MOEMA) with the hope to better solve some or new problems. It is worth to highlight that the concept of multi-objective BMO has been proposed recently by [15]. This is proved that the enhancement of current bio inspired algorithms can be an active topic of research. The implementation of crowding distance and pareto optimal front is similar except that an improvement of crowding distance namely rank crowding distance which will be presented in the methodology section. Furthermore, the improvement of Levy distribution into the exploration MOEMA algorithm can lead for bitterness of the searching process in optimization problems. However, from the studies that have been conducted, the performances of this approach depend on the problems to be solved.