Browsing by Author "Suer, Gursel"

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Citation - WoS: 16
Citation - Scopus: 23
A differential evolution algorithm with variable neighborhood search for multidimensional knapsack problem
(Institute of Electrical and Electronics Engineers Inc., 2015) M. Fatih Tasgetiren; Quanke Pan; Damla Kizilay; Gürsel A. Süer; Tasgetiren, M. Fatih; Kizilay, Damla; Pan, Quan-Ke; Suer, Gursel
This paper presents a differential evolution algorithm with a variable neighborhood search to solve the multidimensional knapsack problem. Unlike the studies employing check and repair operators we employ some sophisticated constraint handling methods to enrich the population diversity by taking advantages of infeasible solution within a predetermined threshold. We propose to a variable neighborhood search employing different mutation strategies to generate the trial population. The proposed algorithm in fact works on a continuous domain but these real-values are converted to 0-1 binary values by using the sigmoid function. In order to enhance the solution quality the differential evolution algorithm with a variable neighborhood search is combined with a binary swap local search algorithm. To the best of our knowledge this is the first reported application of the differential evolution algorithm to solve the multidimensional knapsack problem in the literature. The proposed algorithm is tested on a benchmark instances from the OR-Library. Computational results show its efficiency in solving benchmark instances and its superiority to the best performing algorithms from the literature. © 2017 Elsevier B.V. All rights reserved.
Citation - WoS: 12
Citation - Scopus: 17
A Populated Local Search with Differential Evolution for Blocking Flowshop Scheduling Problem
(IEEE, 2015) M. Fatih Tasgetiren; Quan-Ke Pan; Damla Kizilay; Gursel Suer; Tasgetiren, M. Fatih; Kizilay, Damla; Pan, Quan-Ke; Suer, Gursel
This paper presents a populated local search algorithm through a differential evolution algorithm for solving the blocking flowshop scheduling problem under makespan criterion. Iterated greedy and iterated local search algorithms are simple but extremely effective in solving scheduling problems. However these two algorithms have some parameters to be tuned for which it requires a design of experiments with expensive runs. In this paper we propose a novel multi-chromosome solution representation for both local search and differential evolution algorithm which is responsible for providing the parameters of IG and ILS algorithms. In other words these parameters are learned by the differential evolution algorithm in order to guide the local search process. We also present the greedy randomized adaptive search procedure (GRASP) for the problem on hand. The performance of the populated local search algorithm with differential evolution algorithm and the GRASP heuristic is tested on Taillard's benchmark suite and compared to the best performing algorithms from the literature. Ultimately 90 out of 120 problem instances are further improved.