Electric traction motors in automotive applications work in operational conditions
characterized by variable load, rotational speed and other external conditions: this …

This paper investigates the performance of four multi-objective optimization algorithms,
namely non-dominated sorting genetic algorithm II (NSGA-II), multi-objective particle swarm …

Multi-objective optimization problems (MOPs) involve optimizing multiple conflicting
objectives simultaneously, resulting in a set of Pareto optimal solutions. Due to the high …

The insertion of atypical solutions (immigrants) in Evolutionary Algorithms populations is a
well studied and successful strategy to cope with the difficulties of tracking optima in …

This paper uses evolutionary optimization algorithms to study the multi‐objective
optimization of mechanically stabilized earth (MSE) retaining walls. Five multi‐objective …

This paper proposes an evolutionary wall-following hexapod robot, where a new
multiobjective evolutionary fuzzy control approach is proposed to control both walking …

Multi-objective optimization problem resolution using Evolutionary Algorithms (EAs) has not
yet been completely addressed. Issues such as the population diversity loss and the EA …

This study proposes a new methodology for selecting renewal plans for sewer networks
based on their impacts on the behavior of the networks over their whole lifecycle. The …

To solve dynamic multi-objective optimization problems, a dynamic multi-objective
evolutionary algorithm (DMOEA) must be able to deal with the dynamics of the environment …

In several applications, a solution must be selected from a set of tradeoff alternatives for
operating in dynamic and noisy environments. In this paper, such multicriteria decision …