ABC, a new performance tool for algorithms solving dynamic optimization problems

Alba, Enrique; Sarasola, Briseida

doi:10.1109/cec.2010.5586406

Cited by 13 publications

(4 citation statements)

References 7 publications

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“…where G is the total number of generations and the pA 1 (x) and pA 2 (x) are the functions (for algorithms A1 and A2) that are to be replaced with a measure of population quality including average best of generation, offline performance value, offline error value [1]. ABC value can be positive and negative, where a negative value indicates that algorithm A1 performs better than A2 for a minimization problem.…”

Section: Resultsmentioning

confidence: 99%

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Evolutionary Dynamic Optimization Techniques for Marine Contamination Problem

Altin

Topcuoglu

Ermis

2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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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.

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Section: Resultsmentioning

confidence: 99%

“…As our last experiment, we consider the area between curves (ABC) metric [1], which is a new metric that quantifies the distance between the performance curves of each pair of algorithms. It is the difference of area below performance curves, which is calculated with the trapezoidal method for consecu- …”

Section: Resultsmentioning

confidence: 99%

Evolutionary Dynamic Optimization Techniques for Marine Contamination Problem

Altin

Topcuoglu

Ermis

2015

Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation

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“…Contrarily to this pattern, the area between curves (ABC) [62] is a behavior-based metric that does not require any assumptions about optimal solutions. This is illustrated in Figure 1, which depicts the performance fronts of two dynamic optimizers (left-most plots).…”

Section: Assessing the Anytime Behavior Of A Dynamic Optimizermentioning

confidence: 99%

A computational study on ant colony optimization for the traveling salesman problem with dynamic demands

Oliveira

Bezerra

Stützle

et al. 2021

Computers & Operations Research

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“…They are: final-quality based and behaviorbased measures. However, both of them have been indiscriminately assessed concerning number of iterations, function evaluations, or solutions generated [25,21,7,22,1,2,18]. The rationale behind these resource consumption measures his understanding by how much algorithms are able to improve solutions within a given iteration, or after a fixed number of function evaluations/solutions generated.…”

Section: Experimental Studymentioning

confidence: 99%

The Hypervolume Indicator as a Performance Measure in Dynamic Optimization

Oliveira

Wanner

Souza

et al. 2019

Lecture Notes in Computer Science

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In many real world problems the quality of solutions needs to be evaluated at least according to a bi-objective non-dominated front, where the goal is to optimize solution quality using as little computational resources as possible. This is even more important in the context of dynamic optimization, where quickly addressing problem changes is critical. In this work, we relate approaches for the performance assessment of dynamic optimization algorithms to the existing literature on bi-objective optimization. In particular, we introduce and investigate the use of the hypervolume indicator to compare the performance of algorithms applied to dynamic optimization problems. As a case study, we compare variants of a state-of-the-art dynamic ant colony algorithm on the traveling salesman problem with dynamic demands (DDTSP). Results demonstrate that our proposed approach accurately measures the desirable characteristics one expects from a dynamic optimizer and provides more insights than existing alternatives.

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ABC, a new performance tool for algorithms solving dynamic optimization problems

Cited by 13 publications

References 7 publications

Evolutionary Dynamic Optimization Techniques for Marine Contamination Problem

Evolutionary Dynamic Optimization Techniques for Marine Contamination Problem

A computational study on ant colony optimization for the traveling salesman problem with dynamic demands

The Hypervolume Indicator as a Performance Measure in Dynamic Optimization

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