Abstract Ant Colony Optimization (ACO) is a metaheuristic that is inspired by the pheromone
trail laying and following behavior of some ant species. Artificial ants in ACO are stochastic …

Cloud workflow scheduling is significantly challenging due to not only the large scale of
workflow but also the elasticity and heterogeneity of cloud resources. Moreover, the pricing …

Modern optimization algorithms typically require the setting of a large number of parameters
to optimize their performance. The immediate goal of automatic algorithm configuration is to …

Ant colony optimization (ACO) algorithm is one of the most popular swarm-based algorithms
inspired by the behavior of an ant colony to find the shortest path for food. The multi …

In this paper, an algorithm for energy management system (EMS) based on multi-layer ant
colony optimization (EMS-MACO) is presented to find energy scheduling in Microgrid (MG) …

The simultaneous optimization of many objectives (in excess of 3), in order to obtain a full
and satisfactory set of tradeoff solutions to support a posteriori decision making, remains a …

Human intelligence is the intellectual prowess of humans, which is marked by four basic and
important abilities: learning ability, cognition (acquiring and storing knowledge) ability …

Simulated Annealing (SA) is one of the oldest metaheuristics and has been adapted to solve
many combinatorial optimization problems. Over the years, many authors have proposed …

In the classic distributed permutation flowshop scheduling problem (DPFSP), jobs are
viewed as individual entities and processed independently. In many practical cases …

The particle swarm optimization (PSO) algorithm has been the object of many studies and
modifications for more than 25 years. Ranging from small refinements to the incorporation of …