Metaheuristic algorithms are computational intelligence paradigms especially used for
sophisticated solving optimization problems. This chapter aims to review of all …

This e-book is devoted to global optimization algorithms, which are methods to find optimal
solutions for given problems. It especially focuses on Evolutionary Computation by …

The framework of multiobjective optimization is used to tackle the unsupervised learning
problem, data clustering, following a formulation first proposed in the statistics literature. The …

The difficulty to solve multiple objective combinatorial optimization problems with traditional
techniques has urged researchers to look for alternative, better performing approaches for …

A multi-objective optimization problem can be solved by decomposing it into one or more
single objective subproblems in some multi-objective metaheuristic algorithms. Each …

Multiobjective combinatorial optimization problems (MCOPs) are combinatorial problems
that involve the optimization of several, typically conflicting objectives. When designing a …

Construction planners often face the challenge of optimum resource utilization to
compromise between different and usually conflicting aspects of projects. Time, cost and …

Solutions to real-life optimization problems usually have to be evaluated considering
multiple conflicting objectives. These kind of problems, known as multi-objective …

In this paper, the problem of unsupervised feature selection and its formulation as a
multiobjective optimization problem are investigated. Two existing multiobjective methods …

This paper presents an accelerated proximal gradient method for multiobjective
optimization, in which each objective function is the sum of a continuously differentiable …