Many metaheuristic algorithms are nature-inspired, and most are population-based. Particle swarm optimization is a good example as an efficient metaheuristic algorithm. Inspired by PSO, many new algorithms have been developed in recent years. For example, firefly algorithm was inspired by the flashing behaviour of fireflies. In this paper, the author extends the standard firefly algorithm furth...

This paper addresses a scheduling problem with unrelated parallel machines, sequence dependent setups and due dates. The problem is based on a real case and the objective is to minimise the sum of the makespan and the weighted delays. A mixed integer linear programming model is proposed and since the model considers realistic constraints, a metaheuristic based on GRASP is used as a solution met...

Controlled tabular adjustment is an SDL methodology based on a mixed integer linear programming model. We develop new hybrid heuristics and new metaheuristic learning approaches for solving this model, and examine their performance. Our new approaches are based on partitioning the problem into its discrete and continuous components, and first creating a hybrid that reduces the number of binary ...

This paper describes an object-oriented software system for continuous optimization by a modified artificial bee colony (ABC) metaheuristic. Karaboga’s ABC algorithm was successfully used on many optimization problems and there is also a corresponding program in C. We implemented a modified version in C# which is easier for maintenance since it is object-oriented and which uses threads and sign...

This paper presents an object-oriented software system that implements a cuckoo search (CS) metaheuristic for unconstrained optimization problems. Yang and Deb developed cuckoo search algorithm in MATLAB and tested it on some standard benchmark functions as well as on some engineering optimization problems where it showed promising results. We developed our algorithm in JAVA programming languag...

Over the years, several metaheuristics have been developed to solve hard constrained and unconstrained optimization problems. In general, a metaheuristic is proposed and following researches are made to improve the original algorithm. In this paper, we evaluate a not so new metaheuristic called differential evolution (DE) to solve constrained engineering design problems and compare the results ...

The incorporation of data mining techniques into metaheuristics has been efficiently adopted to solve several optimization problems. Nevertheless, we observe in the literature that this hybridization has been limited to problems in which the solutions are characterized by sets of (unordered) elements. In this work, we develop a hybrid data mining metaheuristic to solve a problem for which solut...

In this paper we use a recently proposed metaheuristic, the Ant System, to solve the Vehicle Routing Problem (VRP) in its basic form, i.e. with capacity and distance restrictions, one central depot and identical vehicles. A \hybrid" Ant System algorithm is rst presented and then improved using problem speciic information (savings, capacity utilization). Experiments on various aspects of the alg...

In this work, we present a methodology to minimize the number of potential conflicts between aircraft trajectories based on route-slot allocation techniques. The traffic assignment problem is modeled as a combinatorial optimization problem for which two metaheuristic optimization algorithms are developed and implemented. The first algorithm relies on a standard simulated annealing, while the se...

Coordination of multi agent systems remains as a problem since there is no prominent method to completely solve this problem. Metaheuristic agents are specific implementations of multi-agent systems, which imposes working together to solve optimisation problems with metaheuristic algorithms. The idea borrowed from swarm intelligence seems working much better than those implementations suggested...