The simulation-driven metaheuristic algorithms have been successful in solving numerous
problems compared to their deterministic counterparts. Despite this advantage, the …

“Exploration and exploitation are the two cornerstones of problem solving by search.” For
more than a decade, Eiben and Schippers' advocacy for balancing between these two …

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 …

Since its inception in 2014, the Competitive Swarm Optimizer (CSO) has emerged as a
significant advancement in the field of swarm intelligence, particularly in addressing large …

Memetic computing is a subject in computer science which considers complex structures
such as the combination of simple agents and memes, whose evolutionary interactions lead …

The quadratic assignment problem (QAP), one of the most difficult problems in the NP-hard
class, models many real-life problems in several areas such as facilities location, parallel …

Genetic Algorithms (GAs) have become a highly effective tool for solving hard optimization
problems. As their popularity has increased, the number of GA applications has grown in …

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

Real-world optimisation problems are often very complex. Metaheuristics have been
successful in solving many of these problems, but the difficulty in choosing the best …

Delineating the tremendous growth in this area, the Handbook of Approximation Algorithms
and Metaheuristics covers fundamental, theoretical topics as well as advanced, practical …