Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review Page 1
Particle Swarm Optimization for Single Objective Continuous Space Problems: A Review …

Memetic Algorithms (MAs) are computational intelligence structures combining multiple and
various operators in order to address optimization problems. The combination and …

Incremental social learning (ISL) was proposed as a way to improve the scalability of
systems composed of multiple learning agents. In this paper, we show that ISL can be very …

Biogeography-based optimization (BBO) is an evolutionary optimization algorithm that uses
migration to share information among candidate solutions. One limitation of BBO is that it …

Scalable and efficient numerical simulations continue to gain importance, as computation is
firmly established as the third pillar of discovery, alongside theory and experiment …

In metaheuristic optimization, understanding the relationship between problems and
algorithms is important but nontrivial. There has been a growing interest in the literature on …

We show how nearest-better clustering, the core component of the NBC-CMA niching
evolutionary algorithm, is improved by appyling a second heuristic rule. This leads to …

In this article, we benchmark a new hybrid algorithm for continuous optimization on the 28
functions for the CEC 2013 special session and competition on real-parameter optimization …

This paper proposes a novel covariance matrix adaptation evolution strategy (CMA-ES)
variant, named AEALSCE, for single-objective numerical optimization problems in the …

We interpret real-valued black-box optimization problems over continuous domains as black-
box landscapes. The performance of a given optimization heuristic on a given problem …