A multiobjective optimization problem involves several conflicting objectives and has a set of
Pareto optimal solutions. By evolving a population of solutions, multiobjective evolutionary …

This paper presents a state-of-the-art review on multi-objective metaheuristics for multi-
objective discrete optimization problems (MODOPs). The relevant literature source and their …

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

Recently, MOEA/D (multi-objective evolutionary algorithm based on decomposition) has
achieved great success in the field of evolutionary multi-objective optimization and has …

With the rapid growth in the number of motor vehicles, traffic pollution has become an
increasingly serious problem, due to high carbon emission and low load utilization rate. It is …

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 …

To approximate the Pareto front, most existing multiobjective evolutionary algorithms store
the nondominated solutions found so far in the population or in an external archive during …

An important consideration of multiobjective optimization (MOO) is the quantitative metrics
used for defining the optimality of different solution sets, which is also the basic principle for …

Arc routing problems (ARPs) are defined and introduced. Following a brief history of
developments in this area of research, different types of ARPs are described that are …

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 …