A New Approach for Making Use of Negative Learning in Ant Colony Optimization

Nurcahyadi, Teddy; Blum, Christian

doi:10.1007/978-3-030-60376-2_2

Cited by 5 publications

(3 citation statements)

References 19 publications

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“…Due to benefits, after the initial publication of the STN methodology in 2021 [8], some researchers have already started using it for enhancing their algorithmic studies. Examples include [10][11][12][13][14][15]. In [13], for example, the author used STN graphics especially for studying the overlap between trajectories of different optimization algorithms.…”

Section: Impact Overviewmentioning

confidence: 99%

“…In contrast, in [12] the authors used STN graphics for explaining why their hybrid algorithm outperformed the standard algorithm variant on some problem instances, while it was outperformed by the standard algorithm on other problem instances. In a similar way, the author of [14] made use of STN graphics for being able to give a more detailed explanation of why some of his algorithm variants outperformed others on problem instances of a certain structure.…”

Section: Impact Overviewmentioning

confidence: 99%

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STNWeb: A new visualization tool for analyzing optimization algorithms

Sartori,

Blum,

Ochoa

2023

SIGEVOlution

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STNWeb (https://www.stn-analytics.com/) is a new web tool for the visualization of the behavior of optimization algorithms such as metaheuristics. It allows for the graphical analysis of multiple runs of multiple algorithms on the same problem instance and, in this way, it facilitates the understanding of algorithm behavior. It may help, for example, in identifying the reasons for a rather low algorithm performance. This, in turn, can help the algorithm designer to change the algorithm in order to improve its performance. STNWeb is designed to be user-friendly. Moreover, it is offered for free to the research community. An article summarizing STNWeb has been recently published in the Software Impacts Journal.

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Section: Impact Overviewmentioning

confidence: 99%

Section: Impact Overviewmentioning

confidence: 99%

STNWeb: A new visualization tool for analyzing optimization algorithms

Sartori,

Blum,

Ochoa

2023

SIGEVOlution

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show abstract

“…We have tested this mechanism in a preliminary work [31] by applying it to the socalled capacitated minimum dominating set problem (CapMDS), with excellent results. In this extended work we first describe the mechanism in general terms in the context of subset selection problems, which is a large class of CO problems.…”

Section: Contribution and General Ideamentioning

confidence: 99%

Adding Negative Learning to Ant Colony Optimization: A Comprehensive Study

Nurcahyadi

Blum

2021

Mathematics

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Ant colony optimization is a metaheuristic that is mainly used for solving hard combinatorial optimization problems. The distinctive feature of ant colony optimization is a learning mechanism that is based on learning from positive examples. This is also the case in other learning-based metaheuristics such as evolutionary algorithms and particle swarm optimization. Examples from nature, however, indicate that negative learning—in addition to positive learning—can beneficially be used for certain purposes. Several research papers have explored this topic over the last decades in the context of ant colony optimization, mostly with limited success. In this work we present and study an alternative mechanism making use of mathematical programming for the incorporation of negative learning in ant colony optimization. Moreover, we compare our proposal to some well-known existing negative learning approaches from the related literature. Our study considers two classical combinatorial optimization problems: the minimum dominating set problem and the multi dimensional knapsack problem. In both cases we are able to show that our approach significantly improves over standard ant colony optimization and over the competing negative learning mechanisms from the literature.

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Negative Learning Ant Colony Optimization for MaxSAT

Nurcahyadi

Blum

Manyà

2022

Int J Comput Intell Syst

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Recently, a new negative learning variant of ant colony optimization (ACO) has been used to successfully tackle a range of combinatorial optimization problems. For providing stronger evidence of the general applicability of negative learning ACO, we investigate how it can be adapted to solve the Maximum Satisfiability problem (MaxSAT). The structure of MaxSAT is different from the problems considered to date and there exists only a few ACO approaches for MaxSAT. In this paper, we describe three negative learning ACO variants. They differ in the way in which sub-instances are solved at each algorithm iteration to provide negative feedback to the main ACO algorithm. In addition to using IBM ILOG CPLEX, two of these variants use existing MaxSAT solvers for this purpose. The experimental results show that the proposed negative learning ACO variants significantly outperform the baseline ACO as well as IBM ILOG CPLEX and the two MaxSAT solvers. This result is of special interest because it shows that negative learning ACO can be used to improve over the results of existing solvers by internally using them to solve smaller sub-instances.

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A New Approach for Making Use of Negative Learning in Ant Colony Optimization

Cited by 5 publications

References 19 publications

STNWeb: A new visualization tool for analyzing optimization algorithms

STNWeb: A new visualization tool for analyzing optimization algorithms

Adding Negative Learning to Ant Colony Optimization: A Comprehensive Study

Negative Learning Ant Colony Optimization for MaxSAT

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