A Projection-Based Evolutionary Algorithm for Multi-Objective and Many-Objective Optimization

Abstract

Many-objective optimization problems (MaOPs) are challenging optimization problems in scientific research. Research has tended to focus on algorithms rather than algorithm frameworks. In this paper, we introduce a projection-based evolutionary algorithm, MOEA/PII. Applying the idea of dimension reduction and decomposition, it divides the objective space into projection plane and free dimension(s). The balance between convergence and diversity is maintained using a Bi-Elite queue. The MOEA/PII is not only an algorithm, but also an algorithm framework. We can choose a decomposition-based or dominance-based algorithm to be the free dimension algorithm. When it is an algorithm framework, it exhibits a better performance. We compare the performance of the algorithm and the algorithm with the MOEA/PII framework. The performance is evaluated by benchmark test instances DTLZ1-7 and WFG1-9 on 3, 5, 8, 10, and 15 objectives using IGD-metric and HV-metric. In addition, we investigated its superior performance on the wireless sensor networks deployment problem using C-metric. Moreover, determining objective domain for the objects of the wireless sensor networks deployment problem reduces the time and makes the solution set more responsive to user needs.

Evolutionary Search for Stochastic Optimization with Binary Choice Relations Under Fuzzy Modeling

“…In the paper by Iqbal et al [16], a multiobjective non-linear programming problem in linear programming was proposed. Ensemble deep learning enabled multi-condition generative design of aerial building machine considering uncertainties was presented by Peng et al [17]. The paper by Wang et al [18] proposes one algorithm framework to solve multiobjective optimization problems.…”