Metaheuristics “In the Large”

Swan, Jerry; Adriaensen, Steven; Brownlee, Alexander E. I.; Hammond, Kevin; Johnson, Colin G.; Kheiri, Ahmed; Krawiec, Faustyna; Merelo, J. J.; Minku, Leandro L.; Özcan, Ender; Pappa, Gisele L.; García‐Sánchez, Pablo; Sörensen, Kenneth; Voß, Stefan; Wagner, Markus; White, David R.

doi:10.1016/j.ejor.2021.05.042

Cited by 66 publications

(35 citation statements)

References 53 publications

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“…using existing mathematical programming formulations in public transport. Even in the field of software engineering and metaheuristics this is mostly unexplored (see, e.g., Kendall et al 2016;Swan et al 2022). A starting point within software engineering could even be the idea of back-to-back testing (see, e.g., Jörges and Steffen 2014).…”

Section: Mixed-integer Programming and Replication Studiesmentioning

confidence: 99%

“…As this has not yet been extensively studied, we just provide some hint on where this could go, e.g., in the field of metaheuristics and evolutionary programming like López-Ibáñez et al ( 2021), Swan et al (2022) and de Armas et al (2021). Within mathematical programming the arguments used when setting up the MIPLIB library, including quite a few instances from our scheduling domain, should be used as a starting point (see Koch et al 2011;Gleixner et al 2021) even if some countermeasures exist (see Mittelmann 2020).…”

Section: Mixed-integer Programming and Replication Studiesmentioning

confidence: 99%

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Revisiting the richness of integrated vehicle and crew scheduling

Kliewer²,

Nourmohammadzadeh

et al. 2022

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The last decades have seen a considerable move forward regarding integrated vehicle and crew scheduling in various realms (airline industry, public transport). With the continuous improvement of information and communication technology as well as general solvers it has become possible to formulate more and more rich versions of these problems. In public transport, issues like rostering, delay propagation or days-off patterns have become part of these integrated problems. In this paper we aim to revisit an earlier formulation incorporating days-off patterns and investigate whether solvability with standard solvers has now become possible and to which extent the incorporation of other aspects can make the problem setting more rich and still keep the possible solvability in mind. This includes especially issues like delay propagation where in public transport delay propagation usually refers to secondary delays following a (primary) disturbance. Moreover, we investigate a robust version to support the claim that added richness is possible. Numerical results are provided to underline the envisaged advances.

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Section: Mixed-integer Programming and Replication Studiesmentioning

confidence: 99%

Section: Mixed-integer Programming and Replication Studiesmentioning

confidence: 99%

Revisiting the richness of integrated vehicle and crew scheduling

Kliewer²,

Nourmohammadzadeh

et al. 2022

Public Transp

Self Cite

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“…This metaheuristic is supposedly based on musicians playing music together (a solution in Harmony Search lingo is called a "melody", for example), even though this algorithm has been unequivocally demonstrated (Weyland, 2010(Weyland, , 2015 to be a special case of Evolution Strategies, a metaheuristic that predates it by 30 years. The community project "Metaheuristics in the Large" (MitL) has recently formulated a framework that enables combinatorial assembly and comparison of metaheuristics, and thereby also addresses issues of reproducibility and scalability (Swan et al 2020).…”

Section: 1mentioning

confidence: 99%

Research trends in combinatorial optimization

Weinand

Sörensen

Segundo

et al. 2021

Int Trans Operational Res

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Real‐world problems are becoming highly complex and therefore have to be solved with combinatorial optimization (CO) techniques. Motivated by the strong increase in publications on CO, 8393 articles from this research field are subjected to a bibliometric analysis. The corpus of literature is examined using mathematical methods and a novel algorithm for keyword analysis. In addition to the most relevant countries, organizations, and authors as well as their collaborations, the most pertinent CO problems, solution methods, and application areas are presented. Publications on CO focus mainly on the development or enhancement of metaheuristics like genetic algorithms. The increasingly problem‐oriented studies deal particularly with real‐world applications within the energy sector, production sector, or data management, which are of increasing relevance due to various global developments. The demonstration of global research trends in CO can support researchers in identifying the relevant issues regarding this expanding and transforming research area.

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“…Many shortcoming studies that propose new metaheuristic algorithm are trapped into three conditions. The first one is that many shortcoming algorithms use metaphors rather than declare distinct mechanism as the algorithm name [8]. These shortcoming algorithms used nature as the source of inspiration, especially animals, such as marine predator [9], monkey [10], whale [11], deer [12], penguin [13], dolphin [14], grasshopper [15], butterfly [16], and so on.…”

Section: Introductionmentioning

confidence: 99%

Stochastic Komodo Algorithm

2022

IJIES

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A novel metaheuristic algorithm is proposed in this paper, namely stochastic komodo algorithm (SKA). This proposed algorithm is an improved version of Komodo mlipir algorithm (KMA), which is inspired by the behaviour of Komodo during foraging and mating. The improvement is conducted by simplifying the basic form of KMA. Like KMA, it consists of three types of Komodo: big male, female, and small male. Male Komodo focuses on intensification. On the other side, females conduct diversification or intensification based on the search space radius. It eliminates sorting mechanism at the beginning of the iteration. Rather than determined from the quality (fitness score), the distribution of the types of Komodo is conducted stochastically at the beginning of every iteration. This proposed algorithm is then tested by using ten functions. Five functions are unimodal, while the five others are multimodal. The proposed algorithm is also compared with several well-known algorithms: football game-based optimization, hide objects game optimization, cloud-theory-based simulated annealing, harmony search, and KMA. The result shows that this proposed algorithm is very competitive compared with these benchmark algorithms in both unimodal and multimodal functions. A female-dominant formation is proven to achieve optimal result.

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Metaheuristics “In the Large”

Cited by 66 publications

References 53 publications

Revisiting the richness of integrated vehicle and crew scheduling

Revisiting the richness of integrated vehicle and crew scheduling

Research trends in combinatorial optimization

Stochastic Komodo Algorithm

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