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 …

This paper performs an unprecedented comparative study of Metaheuristic optimization
frameworks. As criteria for comparison a set of 271 features grouped in 30 characteristics …

In recent years a number of metaheuristic search techniques have been widely used in
developing structural optimization algorithms. Amongst these techniques are genetic …

This paper investigates the behavior of PSO (particle swarm optimization) and CMA-ES
(covariance matrix adaptation evolution strategy) on ill-conditioned functions. The paper …

This chapter aims to address some of the fundamental issues that are often encountered in
optimization problems, making them difficult to solve. These issues include premature …

This research focuses on conducting multi-objective optimisation (MOO) for a Carnot battery
applied within a conventional parabolic trough concentrating solar power (CSP) plant. The …

In this paper, two variants of the particle swarm optimization (PSO) algorithm are employed
for the identification of Bouc–Wen hysteretic systems. The first variant is simple while the …

Several well-studied issues in the particle swarm optimization algorithm are outlined and
some earlier methods that address these issues are investigated from the theoretical and …

The particle swarm optimization algorithm includes three vectors associated with each
particle: inertia, personal, and social influence vectors. The personal and social influence …

Particle swarm optimization (PSO) is a powerful metaheuristic population-based global
optimization algorithm. However, when it is applied to nonseparable objective functions, its …