Korhan Karabulut

A hybrid genetic algorithm for packing in 3d with deepest bottom left with fill method

Three dimensional bin packing problems arise in industrial applications like container ship loading, pallet loading, plane cargo management and warehouse management, etc. In this paper, a hybrid genetic algorithm (GA) is used for regular 3D strip packing. The Genetic Algorithm is hybridized with the presented Deepest Bottom Left with Fill (DBLF) method. Several heuristic methods have also been used for comparison with the hybrid GA.

A variable iterated greedy algorithm for the traveling salesman problem with time windows

This paper presents a variable iterated greedy algorithm for solving the traveling salesman problem with time windows (TSPTW) to identify a tour minimizing the total travel cost or the makespan, separately. The TSPTW has several practical applications in both production scheduling and logistic operations. The proposed algorithm basically relies on a greedy algorithm generating an increasing number of neighboring solutions through the use of the idea of neighborhood change in variable neighborhood search (VNS) algorithms. In other words, neighboring solutions are generated by destructing a solution component and re-constructing the solution again with variable destruction sizes. In addition, the proposed algorithm is hybridized with a VNS algorithm employing backward and forward 1_Opt local searches to further enhance the solution quality. The performance of the proposed algorithm was tested on several benchmark suites from the literature. Experimental results confirm that the proposed algorithm is either competitive to or even better than the best performing algorithms from the literature. Ultimately, new best-known solutions are obtained for 38 out of 125 problem instances of the recently proposed benchmark suite, whereas 15 out of 31 problem instances are also further improved for the makespan criterion.

A discrete artificial bee colony algorithm for the traveling salesman problem with time windows

This paper presents a discrete artificial bee colony algorithm (DABC) for solving the traveling salesman problem with time windows (TSPTW) in order to minimize the total travel cost of a given tour. TSPTW is a difficult optimization problem arising in both scheduling and logistic applications. The proposed DABC algorithm basically relies on the destruction and construction phases of iterated greedy algorithm to generate neighboring food sources in a framework of ABC algorithm. In addition, it also relies on a classical 1-opt local search algorithm to further enhance the solution quality. The performance of the algorithm was tested on a benchmark set from the literature. Experimental results show that the proposed DABC algorithm is very competitive to or even better than the best performing algorithms from the literature.

A discrete artificial bee colony algorithm for the team orienteering problem with time windows

This paper presents a discrete artificial bee colony algorithm (DABC) for solving the team orienteering problem with time windows (TOPTW). The proposed algorithm employs a destruction and construction procedure to generate neighboring food sources in the framework of the DABC algorithm. In addition, a variable neighborhood descent (VND) algorithm is developed to enhance the solution quality. The performance of the algorithm was tested on a benchmark set from the literature. Experimental results show that the proposed DABC algorithm is competitive to the best performing algorithms from the literature. Ultimately, 11 instances are further improved by the proposed DABC algorithm.

A hybrid iterated greedy algorithm for total tardiness minimization in permutation flowshops

A novel temperature calculation formula for total tardiness minimization objective.A local search that is a random search with insertion and swap neighborhoods.Adaption of speedup method for total flow time objective to total tardiness.343 new best permutations out of 540 instances of the problem set has been found. The permutation flowshop scheduling problem is an NP-hard problem that has practical applications in production facilities and in other areas. An iterated greedy algorithm for solving the permutation flowshop scheduling problem with the objective of minimizing total tardiness is presented in this paper. The proposed iterated greedy algorithm uses a new formula for temperature calculation for acceptance criterion and the algorithm is hybridized with a random search algorithm to further enhance the solution quality. The performance of the proposed method is tested on a set of benchmark problems from the literature and is compared to three versions of the traditional iterated greedy algorithm using the same problem instances. Experimental results show that, the proposed algorithm is superior in performance to the other three iterated greedy algorithm variants. Ultimately, new best known solutions are obtained for 343 out of 540 problem instances.

A Populated Iterated Greedy Algorithm with Inver-Over Operator for Traveling Salesman Problem

In this study, we propose a populated iterated greedy algorithm with an Inver-Over operator to solve the traveling salesman problem. The iterated greedy IG algorithm is mainly based on the central procedures of destruction and construction. The basic idea behind it is to remove some solution components from a current solution and reconstruct them in the partial solution to obtain the complete solution again. In this paper, we apply this idea in a populated manner IGP to the traveling salesman problem TSP. Since the destruction and construction procedure is computationally expensive, we also propose an iteration jumping to an Inver-Over operator during the search process. We applied the proposed algorithm to the well-known 14 TSP instances from TSPLIB. The computational results show that the proposed algorithm is very competitive to the recent best performing algorithms from the literature.

Null control in linear antenna arrays with ensemble differential evolution

This paper describes a synthesis method for null insertion in linear antenna array geometries by using newly proposed ensemble differential evolution (DE) algorithm. The given ensemble DE algorithm uses the advantages of several types of DE algorithms, and fuses them within a single algorithm. In the application, the algorithm searches for the minimization of the difference between the produced radiation pattern of the antenna array and desired radiation pattern, which contains null(s) at some specific aspect angle(s). Simulation results are illustrated for a Chebyshev radiation pattern and the effectiveness of the algorithm is validated. Besides, the results of ensemble DE algorithm are compared with bees algorithm, and the superiority of the proposed algorithm to bees algorithm is demonstrated.

A discrete artificial bee colony algorithm for the permutation flowshop scheduling problem with sequence-dependent setup times

A discrete artificial bee colony (DABC) algorithm for the permutation flowshop scheduling problem with sequence-dependent setup times (PFSP-SDST) is presented in this paper. PFSP-SDST is an important problem that has practical applications in production facilities. The proposed DABC algorithm uses destruction and construction procedure to generate neighboring food sources. In addition, a local search algorithm with insert and swap neighborhoods is used to enhance the solution quality. The main contribution of this work is providing a speedup algorithm for the swap neighborhood. Computational experiments are carried out to test the performance of the algorithm on a benchmark problem set from the literature. Experimental results show that the proposed DABC algorithm utilizing swap neighborhood is very competitive to the best performing algorithms from the literature.