Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
A method for generating a well-distributed Pareto set in nonlinear multiobjective optimization
Journal of Computational and Applied Mathematics
SIAM Journal on Optimization
On improving normal boundary intersection method for generation of Pareto frontier
Structural and Multidisciplinary Optimization
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Multiobjective optimization is one of the key challenges in engineering design process. Since the answer to such problem is not unique, a set of evenly distributed solutions is particularly important for a designer. The Directed Search Domain (DSD) method is a numerical optimization approach that has proven to be efficient enough to tackle such optimization problems. In this paper, we propose two modifications to the DSD approach which make the solution algorithm simpler for program implementation. These modifications are related to the control of the search domain and reformulation of the appropriate single objective optimization problem. As a result, the computational efficiency of the method is increased due to the lower number of objective function evaluations. The capabilities of the new approach are demonstrated on a set of test cases.