Workshop on Computational Optimization,Modelling and Simulation (COMS) Session 1

Time and Date: 10:35 - 12:15 on 12th June 2017

Room: HG D 5.2

Chair: Xin-She Yang

203 Global Convergence Analysis of the Flower Pollination Algorithm: A Discrete-Time Markov Chain Approach [abstract]
Abstract: Flower pollination algorithm is a recent metaheuristic algorithm for solving nonlinear global optimization problems. The algorithm has also been extended to solve multiobjective optimization with promising results. In this work, we analyze this algorithm mathematically and prove its convergence properties by using Markov chain theory. By constructing the appropriate transition probability for a population of flower pollen and using the homogeneity property, it can be shown that the constructed stochastic sequences can converge to the optimal set. Under the two proper conditions for convergence, it is proved that the simplified flower algorithm can indeed satisfy these convergence conditions and thus the global convergence of this algorithm can be guaranteed. Numerical experiments are used to demonstrate that the flower pollination algorithm can converge quickly and can thus achieve global optimality efficiently.
Xingshi He, Xin-She Yang and Mehmet Karamanoglu
217 Memetic Simulated Annealing for Data Approximation with Local-Support Curves [abstract]
Abstract: This paper introduces a new memetic optimization algorithm called MeSA (Memetic Simulated Annealing) to address the data fitting problem with local-support free-form curves. The proposed method hybridizes simulated annealing with the COBYLA local search optimization method. This approach is further combined with the centripetal parameterization and the Bayesian information criterion to compute all free variables of the curve reconstruction problem with B-splines. The performance of our approach is evaluated by its application to four different shapes with local deformations and different degrees of noise and density of data points. The MeSA method has also been compared to the non-memetic version of SA. Our results show that MeSA is able to reconstruct the underlying shape of data even in the presence of noise and low density point clouds. It also outperforms SA for all the examples in this paper.
Carlos Loucera, Andres Iglesias Prieto and Akemi Galvez-Tomida
326 A Matheuristic Approach for Solving the Dynamic Facility Layout Problem [abstract]
Abstract: The Dynamic Facility Layout Problem (DFLP) is designing a facility over a multi-period planning horizon where the interdepartmental material flows change from one period to the next one due to changes in product demands. The DFLP is used while designing manufacturing and logistics facilities over multiple planning periods; however, it is a very challenging nonlinear optimization problem. In this paper, a zone-based block layout is used to design manufacturing and logistics facilities over multiple planning periods. A zone-based block layout inherently includes possible aisle structures, which can easily be adapted to different material handling systems. The unequal area DFLP is modeled and solved using a zone-based structure where the dimensions of the departments are decision variables and the departments are assigned to flexible zones with a pre-structured positioning. A matheuristic approach, which combines concepts from Tabu Search (TS) and mathematical programming, is proposed to solve the zone-based DFLP on the continuous plane with unequal area departments. The TS determines the relative locations of departments and their assignments to zones while their exact locations and shapes are calculated by the mathematical programming. Numerical results for a set of test problems from the literature showed that our proposed matheuristic approach is promising.
Sadan Kulturel-Konak
64 Job-flow Anticipation Scheduling in Grid [abstract]
Abstract: In this paper, a heuristic user job-flow scheduling approach for Grid virtual organizations with non-dedicated resources is discussed. Users' and resource providers' preferences, virtual organization's internal policies, resources geographical distribution along with local private utilization impose specific requirements for efficient scheduling according to different, usually contradictive, criteria. With increasing resources utilization level the available resources set and corresponding decision space are reduced. This further complicates the task of efficient scheduling. In order to improve overall scheduling efficiency we propose a heuristic anticipation scheduling approach. Initially it generates a near optimal but infeasible scheduling solution which is then used as a reference for efficient resources allocation.
Victor V. Toporkov, Dmitry Yemelyanov and Alexander Bobchenkov