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

Multi-population based nature-inspired optimization algorithms have attracted wide research
interests in the last decade, and become one of the frequently used methods to handle real …

Recent advances in structural technology require greater accuracy, efficiency and speed in
design of structural systems. It is therefore not surprising that new methods have been …

In recent decades, the field of multiobjective optimization has attracted considerable interest
among evolutionary computation researchers. One of the main features that makes …

Various real-world multiobjective optimization problems are dynamic, requiring evolutionary
algorithms (EAs) to be able to rapidly track the moving Pareto front of an optimization …

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 …

This work presents a multi-objective optimization method based on the artificial bee colony,
called the MOABC, for optimizing problems with multiple objectives. The MOABC uses a grid …

Traditional multiobjective evolutionary algorithms (MOEAs) consider multiple objectives as a
whole when solving multiobjective optimization problems (MOPs). However, this …

Managing convergence and diversity is essential in the design of multiobjective particle
swarm optimization (MOPSO) in search of an accurate and well distributed approximation of …

This paper proposes a constraint handling technique for multiobjective evolutionary
algorithms based on an adaptive penalty function and a distance measure. These two …