6533b7cffe1ef96bd1259813

RESEARCH PRODUCT

Memetic Algorithms in Engineering and Design

Andrea CaponioFerrante Neri

subject

Mathematical optimizationOptimization problemLocal optimumbusiness.industryComputer scienceAnt colony optimization algorithmsMathematicsofComputing_NUMERICALANALYSISParticle swarm optimizationMemetic algorithmLocal search (optimization)businessEvolution strategyTabu search

description

When dealing with real-world applications, one often faces non-linear and nondifferentiable optimization problems which do not allow the employment of exact methods. In addition, as highlighted in [104], popular local search methods (e.g. Hooke-Jeeves, Nelder Mead and Rosenbrock) can be ill-suited when the real-world problem is characterized by a complex and highly multi-modal fitness landscape since they tend to converge to local optima. In these situations, population based meta-heuristics can be a reasonable choice, since they have a good potential in detecting high quality solutions. For these reasons, meta-heuristics, such as Genetic Algorithms (GAs), Evolution Strategy (ES), Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Differential Evolution (DE), have been extensively applied in engineering and design problems.

yearjournalcountryeditionlanguage
2012-01-01
https://doi.org/10.1007/978-3-642-23247-3_15