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TOPCUOĞLU, HALUK RAHMİ

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TOPCUOĞLU

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HALUK RAHMİ

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    Impact of sensor-based change detection schemes on the performance of evolutionary dynamic optimization techniques
    (SPRINGER, 2018) TOPCUOĞLU, HALUK RAHMİ; Altin, Lokman; Topcuoglu, Haluk Rahmi
    Evolutionary 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.
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    Performance evaluation of evolutionary heuristics in dynamic environments
    (SPRINGER, 2012) TOPCUOĞLU, HALUK RAHMİ; Ayvaz, Demet; Topcuoglu, Haluk Rahmi; Gurgen, Fikret
    In recent years, there has been a growing interest in applying genetic algorithms to dynamic optimization problems. In this study, we present an extensive performance evaluation and comparison of 13 leading evolutionary algorithms with different characteristics on a common platform by using the moving peaks benchmark and by varying a set of problem parameters including shift length, change frequency, correlation value and number of peaks in the landscape. In order to compare solution quality or the efficiency of algorithms, the results are reported in terms of both offline error metric and dissimilarity factor, our novel comparison metric presented in this paper, which is based on signal similarity. Computational effort of each algorithm is reported in terms of average number of fitness evaluations and the average execution time. Our experimental evaluation indicates that the hybrid methods outperform the related work with respect to quality of solutions for various parameters of the given benchmark problem. Specifically, hybrid methods provide up to 24% improvement with respect to offline error and up to 30% improvement with respect to dissimilarity factor by requiring more computational effort than other methods.
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    A hyper-heuristic based framework for dynamic optimization problems
    (ELSEVIER, 2014) TOPCUOĞLU, HALUK RAHMİ; Topcuoglu, Haluk Rahmi; Ucar, Abdulvahid; Altin, Lokman
    Most 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.