Many-objective optimization has posed a great challenge to the classical Pareto dominance-
based multiobjective evolutionary algorithms (MOEAs). In this paper, an evolutionary …

This letter suggests an approach for decomposing a multiobjective optimization problem
(MOP) into a set of simple multiobjective optimization subproblems. Using this approach, it …

Adaptive operator selection (AOS) is used to determine the application rates of different
operators in an online manner based on their recent performances within an optimization …

Combining ant colony optimization (ACO) and the multiobjective evolutionary algorithm (EA)
based on decomposition (MOEA/D), this paper proposes a multiobjective EA, ie, MOEA/D …

Decomposition of a multiobjective optimization problem (MOP) into several simple
multiobjective subproblems, named multiobjective evolutionary algorithm based on …

The quality of solution sets generated by decomposition-based evolutionary multi-objective
optimisation (EMO) algorithms depends heavily on the consistency between a given …

Multiobjective evolutionary algorithm based on decomposition (MOEA/D), which bridges the
traditional optimization techniques and population-based methods, has become an …

Combining ideas from evolutionary algorithms, decomposition approaches, and Pareto local
search, this paper suggests a simple yet efficient memetic algorithm for combinatorial …

This chapter provides a short overview of the most significant research work that has been
conducted regarding the solution of computationally expensive multi-objective optimization …

In spite of the popularity of the Multi-objective Evolutionary Algorithm based on
Decomposition (MOEA/D), its use in Constrained Multi-objective Optimization Problems …