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Abstract
Many real-world problems involve two types of problem difficulties: 1) multiple, conflicting objectives and 2) a highly complex search space. On the one hand, instead of a single optimal solution, competing goals give rise to a set of compromise solutions, generally denoted as Pareto-optimal. In the absence of preference information, none of the corresponding trade-offs can be said to be better than the others. On the other hand, the search space can be too large and too complex to be solved by exact methods. Thus, efficient optimization strategies are required that are able to deal with both difficulties. Thus in recent years, Artificial Intelligent Optimization Techniques have been a growing interest for solving such complex problems. They model some natural phenomena, based on the principle of the human thought processes they are Neural Networks.
One of the most important problems we face today is multi-objective optimization problems. In this thesis, we focus on how artificial intelligent optimization techniques (such as Neural Networks) solve non-linear programming problems, and investigate the possibility of using Neural Networks to solve multi-objective optimization problem