Person: TOPCUOĞLU, HALUK RAHMİ
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TOPCUOĞLU
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HALUK RAHMİ
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Publication Metadata only Impact of sensor-based change detection schemes on the performance of evolutionary dynamic optimization techniques(SPRINGER, 2018) TOPCUOĞLU, HALUK RAHMİ; Altin, Lokman; Topcuoglu, Haluk RahmiEvolutionary algorithms are among the most common techniques developed to address dynamic optimization problems. They either assume that changes in the environment are known a priori, especially for some benchmark problems, or detect these changes. On the other hand, detecting the points in time where a change occurs in the landscape is a critical issue. In this paper, we investigate the performance evaluation of various sensor-based detection schemes on the moving peaks benchmark and the dynamic knapsack problem. Our empirical study validates the performance of the sensor-based detection schemes considered, by using the average rate of correctly identified changes and number of sensors invoked to detect a change. We also propose a new mechanism to evaluate the capability of the detection schemes for determining severity of changes. Additionally, a novel hybrid approach is proposed by integrating the change detection schemes with evolutionary dynamic optimization algorithms in order to set algorithm-specific parameters dynamically. The experimental evaluation validates that our extensions outperform the reference algorithms for various characteristics of dynamism.Publication Metadata only Evolutionary dynamic optimization techniques for marine contamination problem(Association for Computing Machinery, Inc, 2015) TOPCUOĞLU, HALUK RAHMİ; Altin L., Topcuoglu H.R., Ermis M.Marine pollution is the release of by-products that cause harm to natural marine ecosystems and one of the most important sources is the discharge of oil, ballast water from vessels. If the relevant technology is not available, alternative way to monitor environmental pollution is to use unmanned air vehicles (UAVs). Since the navigating vessels move in different directions and speeds, the determination of the tour that should be traveled by a UAV resembles to the dynamic traveling salesman problem (DTSP) in many aspects. This paper addresses a new type of DTSP, where targets can move in different directions with different speeds. The locations of all vessels can change due to changes in velocity that alters the length of all edges. Consequently, this problem has a higher complexity in comparison to classical DTSP presented in the literature. An empirical study is conducted to evaluate performance of selected evolutionary dynamic optimization techniques on solving the problem. © Copyright 2015 ACM.Publication Metadata only A hyper-heuristic based framework for dynamic optimization problems(ELSEVIER, 2014) TOPCUOĞLU, HALUK RAHMİ; Topcuoglu, Haluk Rahmi; Ucar, Abdulvahid; Altin, LokmanMost of the real world problems have dynamic characteristics, where one or more elements of the underlying model for a given problem including the objective, constraints or even environmental parameters may change over time. Hyper-heuristics are problem-independent meta-heuristic techniques that are automating the process of selecting and generating multiple low-level heuristics to solve static combinatorial optimization problems. In this paper, we present a novel hybrid strategy for applicability of hyper-heuristic techniques on dynamic environments by integrating them with the memory/search algorithm. The memory/search algorithm is an important evolutionary technique that have applied on various dynamic optimization problems. We validate performance of our method by considering both the dynamic generalized assignment problem and the moving peaks benchmark. The former problem is extended from the generalized assignment problem by changing resource consumptions, capacity constraints and costs of jobs over time; and the latter one is a well-known synthetic problem that generates and updates a multidimensional landscape consisting of several peaks. Experimental evaluation performed on various instances of the given two problems validates that our hyper-heuristic integrated framework significantly outperforms the memory/search algorithm. (C) 2014 Elsevier B.V. All rights reserved.