On the Performance of Baseline Evolutionary Algorithms on the Dynamic Knapsack Problem

Roostapour, Vahid; Neumann, Aneta; Neumann, Franz‐Josef

doi:10.1007/978-3-319-99253-2_13

Cited by 26 publications

(19 citation statements)

References 10 publications

(17 reference statements)

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“…Dynamic instances of knapsack problems have been proposed before. 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%

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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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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“…For our dynamic constraint bound changes, we follow the approach taken in [18]. We assume that the initial constraint bound is B = 10 and stays within the interval [5,30].…”

Section: The Influence Maximization Problemmentioning

confidence: 99%

Pareto Optimization for Subset Selection with Dynamic Cost Constraints

Roostapour

Neumann

et al. 2019

AAAI

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In this paper, we consider the subset selection problem for function f with constraint bound B which changes over time. We point out that adaptive variants of greedy approaches commonly used in the area of submodular optimization are not able to maintain their approximation quality. Investigating the recently introduced POMC Pareto optimization approach, we show that this algorithm efficiently computes a φ = (α f /2)(1 − 1 e α f )-approximation, where α f is the submodularity ratio of f , for each possible constraint bound b ≤ B. Furthermore, we show that POMC is able to adapt its set of solutions quickly in the case that B increases. Our experimental investigations for the influence maximization in social networks show the advantage of POMC over generalized greedy algorithms.

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“…Studying the performances of evolutionary algorithms for dynamic models of combinatorial optimization problems is an emerging field in evolutionary computation [8,16,19,22,24,26]. Within the paper, we present the dynamic model of the Weighted Vertex Cover problem (WVC), which is simply named Dynamic Weighted Vertex Cover problem (DWVC).…”

Section: Introductionmentioning

confidence: 99%

Runtime Performances of Randomized Search Heuristics for the Dynamic Weighted Vertex Cover Problem

2020

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Randomized search heuristics such as evolutionary algorithms are frequently applied to dynamic combinatorial optimization problems. Within this paper, we present a dynamic model of the classic Weighted Vertex Cover problem and analyze the runtime performances of the well-studied algorithms Randomized Local Search and (1+1) EA adapted to it, to contribute to the theoretical understanding of evolutionary computing for problems with dynamic changes. In our investigations, we use an edge-based representation based on the dual form of the Linear Programming formulation for the problem and study the expected runtime that the adapted algorithms require to maintain a 2-approximate solution when the given weighted graph is modified by an edge-editing or weight-editing operation. Considering the weights on the vertices may be exponentially large with respect to the size of the graph, the step size adaption strategy is incorporated, with or without the 1/5-th rule that is employed to control the increasing/decreasing rate of the step size. Our results show that three of the four algorithms presented in the paper can recompute 2-approximate solutions for the studied dynamic changes in polynomial expected runtime, but the (1+1) EA with 1/5-th Rule requires pseudo-polynomial expected runtime.

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On the Performance of Baseline Evolutionary Algorithms on the Dynamic Knapsack Problem

Cited by 26 publications

References 10 publications

Flexible Wolf Pack Algorithm for Dynamic Multidimensional Knapsack Problems

Flexible Wolf Pack Algorithm for Dynamic Multidimensional Knapsack Problems

Pareto Optimization for Subset Selection with Dynamic Cost Constraints

Runtime Performances of Randomized Search Heuristics for the Dynamic Weighted Vertex Cover Problem

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