Ant Colony Optimization (ACO) is a collection of metaheuristics inspired by foraging in ant colonies, whose aim is to solve combinatorial optimization problems. We identify some principles behind the metaheuristics’ rules; and we show that ensuring their application, as a correction to a published algorithm for the vertex cover problem, leads to a statistically significant improvement in empiri...

Complex optimization problems that cannot be solved using exhaustive search require efficient search metaheuristics to find optimal solutions. In practice, metaheuristics suffer from various types of search bias, the understanding of which is of crucial importance, as it directly pertinent to the problem of making the best possible selection of solvers. Two metrics are introduced in this study:...

Nature-inspiredmetaheuristic algorithms have become powerful and popular in computational intelligence and many applications. There are some important developments in recent years, and this special issue aims to provide a timely review of such developments, including ant colony optimization, bat algorithm, cuckoo search, particle swarm optimization, genetic algorithms, support vector machine, n...

Ant Colony Optimization (ACO) is a class of metaheuristic algorithms sharing the common approach of constructing a solution on the basis of information provided both by a standard constructive heuristic and by previously constructed solutions. This paper is composed of three parts. The first one frames ACO in current trends of research on metaheuristics for combinatorial optimization. The secon...

Large costs arise at a seaport container terminal from the duration of the unloading of vessels and from the time a vessel is waiting to be unloaded. The optimal allocation of vessels to berth space (Berth Allocation Problem) becomes more and more important as its solution is also input to further terminal decision problems. We compare solutions for a realistic data Berth Allocation Problem fou...

Nowadays, dust phenomenon is one of the important challenges in warm and dry areas. Forecasting the phenomenon before its occurrence helps to take precautionary steps to prevent its consequences. Fuzzy expert systems capabilities have been taken into account to assist and cope with the uncertainty associated to complex environments such as dust forecasting problem. This paper presents novel hyb...

This paper studies the hybrid flow shop scheduling where the optimization criterion is the minimization of total tardiness. First, the problem is formulated as a mixed integer linear programming model. Then, to solve large problem sizes, an artificial immune algorithm hybridized with a simple local search in form of simulated annealing is proposed. Two experiments are carried out to evaluate th...

The fixed charge transportation problem (FCTP) is a deployment of the classical transportation problem in which a fixed cost is incurred, independent of the amount transported, along with a variable cost that is proportional to the amount shipped. Since the problem is considered as an NP-hard, the computational time grows exponentially as the size of the problem increases. In this pape...

This paper represents the metaheuristics proposed for solving a class of Shop Scheduling problem. The Bacterial Foraging Optimization algorithm is featured with Ant Colony Optimization algorithm and proposed as a natural inspired computing approach to solve the Mixed Shop Scheduling problem. The Mixed Shop is the combination of Job Shop, Flow Shop and Open Shop scheduling problems. The sample i...

The k-median problem is a well-known strongly NP-hard combinatorial optimization problem of both theoretical and practical significance. The previous best approximation ratio for this problem is 2.611+ǫ (Bryka et al. 2014) based on an (1, 1.95238219) bi-factor approximation algorithm for the classical facility location problem (FLP). This work offers an improved algorithm with an approximation ...