Multi-hard Problems in Uncertain Environment

Przybylek, Michal R.; Wierzbicki, Adam; Michalewicz, Zbigniew

doi:10.1145/2908812.2908814

Cited by 5 publications

(4 citation statements)

References 19 publications

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“…Some problems, due to their complexity, need to be split into subproblems in order to be adequately resolved ("divide to conquest" strategy). Therefore, the problems, considering the number of subproblems, can be classified into two classes: (1) single optimization problems (single-problems), which are composed of only one problem, and (2) multiple optimization problems (multi-problems) [48][49][50], which are a non-trivial arrangement of two or more problems. A point that needs to be emphasized is that a problem can be composed of two subproblems (multi-problems), where the first subproblem has one objective (single objective) and the second subproblem has two objectives (multi-objective).…”

Section: Subproblems (Single-problem Versus Multi-problem)mentioning

confidence: 99%

“…Optimization problems, considering their origin, can be (1) "artificially" created by someone or (2) identified in the real world, requiring efforts to understand and specify them. [16,49,50]. Artificial or designed optimization problems are proposed by someone, generally as an abstraction and reduced view of reality.…”

Section: Artificial Versus Real-world Problemmentioning

confidence: 99%

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Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development

Peres¹,

Castelli²

2021

Applied Sciences

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In the past few decades, metaheuristics have demonstrated their suitability in addressing complex problems over different domains. This success drives the scientific community towards the definition of new and better-performing heuristics and results in an increased interest in this research field. Nevertheless, new studies have been focused on developing new algorithms without providing consolidation of the existing knowledge. Furthermore, the absence of rigor and formalism to classify, design, and develop combinatorial optimization problems and metaheuristics represents a challenge to the field’s progress. This study discusses the main concepts and challenges in this area and proposes a formalism to classify, design, and code combinatorial optimization problems and metaheuristics. We believe these contributions may support the progress of the field and increase the maturity of metaheuristics as problem solvers analogous to other machine learning algorithms.

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Section: Subproblems (Single-problem Versus Multi-problem)mentioning

confidence: 99%

Section: Artificial Versus Real-world Problemmentioning

confidence: 99%

Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development

Peres¹,

Castelli²

2021

Applied Sciences

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“…Given that both TSP and KP have been widely studied in the literature, this knowledge sets the basis for dealing with the interactions arising in TTP. Different heuristic approaches have been proposed for variants of TTP (Yafrani and Ahiod 2016; Chand and Wagner 2016; Mei, Li, and Yao 2014) and a large benchmark set to compare these algorithms has been established in (Polyakovskiy et al 2014;Przybylek, Wierzbicki, and Michalewicz 2016). Recent investigations have considered the impact of the renting rate which connects the TSP and KP part of the problem (Wu, Polyakovskiy, and Neumann 2016).…”

Section: Combinatorial Optimizationmentioning

confidence: 99%

What's Hot in Evolutionary Computation

Friedrich

Neumann

2017

AAAI

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“…The simplicity of our models makes them suitable for mobile and embedded architectures, where computational power and space are limited. We note that other methods inspired by social observations were successfully used in the past, for example: [4] and its extensions [23], [22].…”

Section: Introductionmentioning

confidence: 99%

Crowd counting´a la Bourdieu: Automated estimation of the number of people

Przybylek

Shkroba

2020

COMSIS J

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In recent years, sociologists have taught us how important and emergent the problem of crowd counting is. They have recognised a variety of reasons for this fact, including: public safety (e.g. crushing between people, trampling underfoot, risk of spreading infectious disease, aggression), politics (e.g. police and government tend to underestimate the number of people, whilst protest organisers tend to overestimate it) and journalism (e.g. accuracy of the estimation of the ground truth supporting an article). The aim of this paper is to investigate models for crowd counting that are inspired by the observations of famous sociologist Pierre Bourdieu. We show that despite the simplicity of the models, we can achieve competitive result. This makes them suitable for low computational power and energy efficient architectures.

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Multi-hard Problems in Uncertain Environment

Cited by 5 publications

References 19 publications

Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development

Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development

What's Hot in Evolutionary Computation

Crowd counting´a la Bourdieu: Automated estimation of the number of people

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