DOCEA/D: Dual-Operator-based Constrained many-objective Evolutionary Algorithm based on Decomposition

Qasim, Syed Zaffar; Ismail, Muhammad Ali

doi:10.1007/s10586-022-03647-7

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…Such problems are commonly called as many-objective optimization problems [1,[19][20][21]. Most computing and software engineering problems identified in various surveys [22][23][24][25] involve optimizing more than five objectives. For instance, the software project portfolio optimization problem [26] involves about eight goals which include maximization of potential revenue, minimizing the available resources, strategic similarity of projects with software development organization, maximization of positive as well as minimization of negative synergy effects with-inthe projects selected for a portfolio.…”

Section: Many-objective Optimizationmentioning

confidence: 99%

FixedSum: An improved algorithm for generation of weight vectors for decomposition-based multiobjective optimization

Qasim

Ismail

2023

Preprint

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Many multiobjective optimization algorithms employ weight vectors for decomposing a problem into multiple subproblems by associating each subproblem with a single weight vector (WV). These weight vectors should be uniformly spread in the space so as to get a diversified set of solutions along the Pareto front. In the recent past, different studies involving the development of decomposition-based multiobjective evolutionary algorithms (including MOEA/D and MOEA/DD) have adopted different methods for generating weight vectors. However these WVs lack the needed diversity and are either biased near the boundary or the inner regions of the search space. In this paper, we have proposed an improved algorithm, FixedSum, for the generation of weight vectors and graphically compared its results with other methods on 2-, 5-, 8- and 10-D weight vectors. For further validation of our approach, we have applied our weight vectors along with WVs of two other methods for solving the DTLZ4 problem using a popular decomposition-based algorithm MOEA/DD. All the results are graphically shown which demonstrate the improved spread ability of our approach as compared to the competing approaches.

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