The Role of Representations in Dynamic Knapsack Problems

Branke, Jürgen; Orbayı, Merve; Uyar, Şima

doi:10.1007/11732242_74

Cited by 22 publications

(13 citation statements)

References 12 publications

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“…In particular, the role of the representation has been discussed but no experimental work has been carried out to extend the existing work on this subject (Ursem et al 2002;Branke et al 2005Branke et al , 2006. In addition, it would also be interesting to investigate the impact of different penalty functions, especially those that allow the algorithm to explore the infeasible region.…”

Section: Discussionmentioning

confidence: 97%

Dynamic combinatorial optimisation problems: an analysis of the subset sum problem

Rohlfshagen

Yao

2010

Soft Comput

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The field of evolutionary computation has traditionally focused on static optimisation problems. Recently, many new approaches have been proposed that adapt traditional evolutionary algorithms to the dynamic domain to deal with the task of tracking high-quality solutions as the search space changes over time. These novel algorithms are subsequently evaluated on a wide range of different optimisation problems, including wellspecified benchmark generators. However, due to a lack of theoretical results, as well as a general lack of references to actual real-world scenarios, it is not entirely clear whether these benchmarks capture any of the characteristics found in NP-hard dynamic optimisation problems. In this paper, we extensively analyse the properties of the NP-hard (dynamic) subset sum problem. In particular, we highlight the correlation between the dynamic parameters of the problem and the resulting movement of the global optimum. It is shown by empirical means that the degree to which the global optimum moves in response to the underlying dynamics is correlated only in specific cases. Furthermore, the role of the representation used to encode the problem, as well as the impact of the formulation of the objective function on the dynamics are also discussed.

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Section: Discussionmentioning

confidence: 97%

Dynamic combinatorial optimisation problems: an analysis of the subset sum problem

Rohlfshagen

Yao

2010

Soft Comput

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“…In addition to the above runtime analysis of EDO methodologies, there are also theoretical analysis of dynamic fitness landscape [21,22,80,81,83,84,88,89,100,101]. Readers are referred to [71] for a literature review on research in this area.…”

Section: Theoretical Development Of Edo Methodologiesmentioning

confidence: 99%

Evolutionary Dynamic Optimization: Methodologies

Nguyễn

Yang

Branke

et al. 2013

Studies in Computational Intelligence

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Abstract. In recent years, Evolutionary Dynamic Optimization (EDO) has attracted a lot of research effort and has become one of the most active research areas in evolutionary computation (EC) in terms of the number of activities and publications. This chapter provides a summary of main EDO approaches in solving DOPs. The strength and weakness of each approach and their suitability for different types of DOPs are discussed. Current gaps, challenging issues and future directions regarding EDO methodolgies are also presented.

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“…However, these studies are mainly focused on either only one dimension problem or a cyclic change of the resource constraint [35,36]. Inspiration from [13,37], we construct the dynamic MKP by updating all parameters of w kj , p j , and c k after a predefined simulation time unit using a normally distributed random distribution with zero mean and standard deviation θ:…”

Section: Definition Of the Dynamic Multidimensional Knapsackmentioning

confidence: 99%

“…In formula (4), p + j , w + kj , and c + k denote the updated parameters of MKP when a change occurs after a predefined simulation time units, respectively. The less number of simulation time units yield to more frequent changes and vice versa [13,[37][38][39]. The number of iterations allocated for each environment is usually adopted as the frequency of changes.…”

Section: Definition Of the Dynamic Multidimensional Knapsackmentioning

confidence: 99%

Flexible Wolf Pack Algorithm for Dynamic Multidimensional Knapsack Problems

Xiao

2020

Research

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Optimization problems especially in a dynamic environment is a hot research area that has attracted notable attention in the past decades. It is clear from the dynamic optimization literatures that most of the efforts have been devoted to continuous dynamic optimization problems although the majority of the real-life problems are combinatorial. Moreover, many algorithms shown to be successful in stationary combinatorial optimization problems commonly have mediocre performance in a dynamic environment. In this study, based on binary wolf pack algorithm (BWPA), combining with flexible population updating strategy, a flexible binary wolf pack algorithm (FWPA) is proposed. Then, FWPA is used to solve a set of static multidimensional knapsack benchmarks and several dynamic multidimensional knapsack problems, which have numerous practical applications. To the best of our knowledge, this paper constitutes the first study on the performance of WPA on a dynamic combinatorial problem. By comparing two state-of-the-art algorithms with the basic BWPA, the simulation experimental results demonstrate that FWPA can be considered as a feasibility and competitive algorithm for dynamic optimization problems.

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The Role of Representations in Dynamic Knapsack Problems

Cited by 22 publications

References 12 publications

Dynamic combinatorial optimisation problems: an analysis of the subset sum problem

Dynamic combinatorial optimisation problems: an analysis of the subset sum problem

Evolutionary Dynamic Optimization: Methodologies

Flexible Wolf Pack Algorithm for Dynamic Multidimensional Knapsack Problems

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