COMPARATIVE STUDY ON DIFFERENT TECHNIQUES FOR SOLVING MULTI OBJECTIVE PROBLEMS OF OPTIMIZATION

During the last 15 years, multi-objective mathematical programming has been one of the fastest developing fields of personal view. Following the use of evolutionary methods over single-objective optimization for a longer period of time, more than two decades, the wellness industry has incorporated several goals. The function has finally gained attention as a field of science. As a result, many people variations of current strategies and new evolutionary-based methods have been developed. In the scientific articles, it was recently written. In this paper, we suggest a multi-objective optimization algorithm that is based on antibody production theorem (either constrained or unconstrained). The aim of this paper is to summarise and compile details on these existing methods, stressing the relevance of analyzing operations analysis strategies, which are used by the majority of them. In an effort to entice scholars to explore these problems, they are focused. Approaches to mathematical analysis for new ways to utilize the scope evolved algorithms’ features. An overview of the key algorithms behind these methods is also given, as well as a short critique that includes their benefits and drawbacks, applicability, and some recommendations applications that are well-known. Finally, future developments in this field are discussed, as well as any potential future directions, additional analysis is also being considered. According to the findings, the suggested strategy seems to be a feasible solution for solving multi-objective optimization problems.