A NOVEL APPROACH TO MULTI-OBJECTIVE OPTIMIZATION USING SIMILARITY MEASURES AND ENSEMBLE LEARNING
Keywords:
Optimization, Consuming, Generalizable, Balanced Solution, StandardAbstract
Most multi-objective optimization faces multiple-objective problems, such that more than one goal will only be achieved concurrently. Consequently, multi-objective optimization can be much more time-consuming and inefficient if not tackled using conventional approaches with a single optimization tool. This paper proposes a new approach by combining similarity measures and ensemble learning to enhance the efficiency of multi-objective optimization. We first define similarity measures to measure the degree of closeness between different solutions. We can use this to find the solutions that are close and then use it to help find more similar solutions during the search process. This not only reduces the search space but also aids in finding generalizable and balanced solutions. The second thing is that we use ensemble learning, combining several models for a more robust and accurate outcome. This paper introduces ensemble learning into multi-objective optimization to combine various single-objective algorithms for more diversified solutions. We tested our proposed methodology on a suite of standard multi-objective optimization problems, and results demonstrate better efficiency in finding the actual (or trade-off) Pareto front and converging closer to it compared with that done by traditional single-algorithm approaches. This paper illustrates the solution of multi-objective optimization problems with our proposed method and shows promising results.
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Copyright (c) 2024 Divya Beeram (Author)
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