Particle swarm optimization (PSO) is one of the most well-regarded swarm-based algorithms
in the literature. Although the original PSO has shown good optimization performance, it still …

Multi-objective formulations are realistic models for many complex engineering optimization
problems. In many real-life problems, objectives under consideration conflict with each …

Due to the novelty of the Grey Wolf Optimizer (GWO), there is no study in the literature to
design a multi-objective version of this algorithm. This paper proposes a Multi-Objective …

Problems with multiple objectives arise in a natural fashion in most disciplines and their
solution has been a challenge to researchers for a long time. Despite the considerable …

Multi-objective evolutionary algorithms (MOEAs) that use non-dominated sorting and
sharing have been criticized mainly for:(1) their O (MN/sup 3/) computational complexity …

The Strength Pareto Evolutionary Algorithm (SPEA) is a relatively recent technique for
finding or approximating the Pareto-optimal set for multiobjective optimization problems. In …

Decomposition is a basic strategy in traditional multiobjective optimization. However, it has
not yet been widely used in multiobjective evolutionary optimization. This paper proposes a …

Multi-objective evolutionary algorithms which use non-dominated sorting and sharing have
been mainly criticized for their (i) O (MN 3) computational complexity (where M is the number …

Computational Intelligence: An Introduction, Second Edition offers an in-depth exploration
into the adaptive mechanisms that enable intelligent behaviour in complex and changing …

This work proposes a new multi-objective algorithm inspired from the navigation of grass
hopper swarms in nature. A mathematical model is first employed to model the interaction of …