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

Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter
optimization algorithms in current use. DE operates through similar computational steps as …

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

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 …

This paper compares three modern and two classical multiobjective optimizers (MOOs) as
applied to real-world problems in electromagnetics. The behavior of sophisticated optimizers …

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

As an essential step of metaheuristic optimizers, initialization seriously affects the
convergence speed and solution accuracy. The main motivation of the state-of-the-art …

In the last two decades, multiobjective optimization has become mainstream because of its
wide applicability in a variety of areas such engineering, management, the military and other …

This letter proposes a new technique for realizing a nonuniform linear array with low
sidelobe level (SLL) and beamwidth. The technique utilizes multiple-objective functions for …

Antenna design problems often require the optimization of several conflicting objectives
such as gain maximization, sidelobe level (SLL) reduction and input impedance matching …