Simulated Annealing algorithm (SA) is a well-known probabilistic heuristic. It mimics the
annealing process in metallurgy to approximate the global minimum of an optimization …

The simulated annealing (SA) is amongst the well-known algorithms for stochastic
optimization. Unfortunately, its major weakness is the slow rate of convergence, leading to a …

Abstract The Simulated Annealing (SA) is a stochastic local search algorithm. Its efficiency
involves the adaptation of the cooling law. In this paper, we integrate Hidden Markov Model …

Simulated annealing (SA) is a local search algorithm. It mimics the annealing process used
in the metallurgy to approximate the global optimum of an optimization problem and uses …

In this paper, an evolved ant colony system (ACS) is proposed by dynamically adapting the
responsible parameters for the decay of the pheromone trails 𝜉and 𝜌using fuzzy logic …

In this paper we control the potentials of exploration and exploitation into the Ant Colony
System (ACS) by dynamically adapting the parameter relative to the importance of heuristic …

The population size has a very strong impact on the efficiency, solution quality, and
computational cost in a Swarm Intelligence (SI). In Ant Colony System algorithm, and as a …

Particle swarm optimization is a stochastic population-based metaheuristic algorithm, it been
successful in solving a height range of real-world problems. The primary challenge present …

Nowadays, control is the main concern with emergent behaviours of multi-agent systems
and state machine reasoning. This paper focuses on the restriction of this general issue to …

Simulated annealing (SA) is a well-known stochastic local search algorithm for solving
unconstrained optimization problems. It mimics the annealing process used in the …