In the real world, there are a number of optimization problems whose search space is
restricted to take binary values; however, there are many continuous metaheuristics with …

Over the ages, nature has constantly been a rich source of inspiration for science, with much
still to discover about and learn from. Swarm Intelligence (SI), a major branch of artificial …

In many optimisation problems, all or some of decision variables are discrete. Solving such
problems are more challenging than those problems with pure continuous variables. Among …

Abstract Feature Subset Selection (FSS) is an NP-hard problem to remove redundant and
irrelevant features particularly from medical data, and it can be effectively addressed by …

In the feature selection process, reaching the best subset of features is considered a difficult
task. To deal with the complexity associated with this problem, a sophisticated and robust …

Many metaheuristic approaches have been developed to select effective features from
different medical datasets in a feasible time. However, most of them cannot scale well to …

The development of technologies for the additive manufacturing, in particular of metallic
materials, is offering the possibility of producing parts with complex geometries. This opens …

This paper proposes a modified binary particle swarm optimization (MBPSO) method for
feature selection with the simultaneous optimization of SVM kernel parameter setting …

Particle swarm optimization (PSO) as a novel computational intelligence technique, has
succeeded in many continuous problems. But in discrete or binary version there are still …

Although the particle swarm optimisation (PSO) algorithm requires relatively few parameters
and is computationally simple and easy to implement, it is not a globally convergent …