Highly scalable Erlang framework for agent-based metaheuristic computing

Turek, Wojciech; Stypka, Jan; Krzywicki, Daniel; Anielski, Piotr; Piętak, Kamil; Byrski, Aleksander; Kisiel-Dorohinicki, Marek

doi:10.1016/j.jocs.2016.03.003

Cited by 14 publications

(8 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…in order to utilize properly the available supercomputing infrastructure e.g. [29,30]. Currently, two hybridization of EMAS are researched, namely the one with Differential Evolution (in progress, not published yet) and with Particle Swarm Optimization (preliminary results already published in [26]).…”

Section: Resultsmentioning

confidence: 99%

Fine Tuning of Agent-Based Evolutionary Computing

Mizera

Nowotarski

Byrski

et al. 2018

Journal of Artificial Intelligence and Soft Computing Research

Self Cite

View full text Add to dashboard Cite

Evolutionary Multi-agent System introduced by late Krzysztof Cetnarowicz and developed further at the AGH University of Science and Technology became a reliable optimization system, both proven experimentally and theoretically. This paper follows a work of Byrski further testing and analyzing the efficacy of this metaheuristic based on popular, high-dimensional benchmark functions. The contents of this paper will be useful for anybody willing to apply this computing algorithm to continuous and not only optimization.

show abstract

Section: Resultsmentioning

confidence: 99%

Fine Tuning of Agent-Based Evolutionary Computing

Mizera

Nowotarski

Byrski

et al. 2018

Journal of Artificial Intelligence and Soft Computing Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…An execution model based on the composition of parallel skeletons has been described in the work of Turek et al The Farm skeleton was used to partition the population according to agent behavior and processed each partition in parallel as a meeting arena. The model proved linearly scalable but was semantically equivalent to the sequential one: discrete generations of agents were implied by the iterative transformation of a population (Figure B).…”

Section: Concurrency In Agent‐based Evolutionary Computationsmentioning

confidence: 99%

Concurrent agent‐based evolutionary computations as adaptive dataflows

Krzywicki

Faber

Debski

2018

Concurrency and Computation

Self Cite

View full text Add to dashboard Cite

Summary This paper introduces a new formal description of the execution model for agent‐based computing systems in the form of an adaptive dataflow decoupled from the domain‐specific semantics of the computation. We show that the execution models studied in previous work can be unified in this common model. The parameters of the model such as queuing policies and granularity of the data in the flow are analyzed. Several queueing alternatives are benchmarked to demonstrate how they affect the efficiency of the computation. Using the example of a multi‐agent evolutionary optimisation problem solver, the new approach is shown to outperform the classic one. This proposed model is well suited to functional languages and can be easily mapped onto different classes of hardware – from simple single‐core computers to distributed environments.

show abstract

“…Complex networks [20] are prominently featured in one contribution [13] and other different hard complex optimization problems are considered in four contributions [14][15][16][17]. Finally, one of the contributions [18] tackles the use of agent-based metaheuristic computing, a prime example of complex collective system [21] -see also [22]. A more detailed introduction to these papers is provided next.…”

Section: An Overview Of This Special Issuementioning

confidence: 99%

“…Turek et al [18] focus on the parallelization of agent-based metaheuristic computing. To this end, they consider a framework in Erlang, the concurrent functional programming language.…”

Section: An Overview Of This Special Issuementioning

confidence: 99%

“…This thematic special issue revolves around the intersection of metaheuristic optimization techniques and complex systems from two different perspectives, namely the use of metaheuristics as a tool for analyzing, modeling or designing complex systems, or the utilization of metaheuristics approaches which are themselves complex systems due to its particular internal structure. We have gathered six papers [13][14][15][16][17][18] targeted to cover algorithmic and implementation aspects of such complex meta-heuristics in both discrete and continuous domains, as well as applications to complex systems. Some contributions to this thematic special issue are extended versions of results communicated at the EvoCOMPLEX track of the EvoApplications conference [19], held in Copenhagen, 8-10 April 2015 as a part of the EvoStar event.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Complex metaheuristics

Cotta

Schaefer

2016

Journal of Computational Science

View full text Add to dashboard Cite

a b s t r a c tComplexity is a prevalent feature of numerous natural and artificial systems and as such has attracted much scientific interest in the last decades. The pursuit of computational tools capable of analyzing, modeling or designing systems exhibiting this complex nature -in which the properties of the system are not evident at the bottom level but emerge from its global structure -is a major issue. Metaheuristics can play here an important role due to its intrinsic adaptability and powerful optimization capabilities. In many regards, metaheuristics are also examples of complex systems since their behavior emanates from the orchestrated interplay of simpler algorithmic components. This bidirectional connection between metaheuristics and complex systems offers numerous avenues for fruitful research.

show abstract

Highly scalable Erlang framework for agent-based metaheuristic computing

Cited by 14 publications

References 19 publications

Fine Tuning of Agent-Based Evolutionary Computing

Fine Tuning of Agent-Based Evolutionary Computing

Concurrent agent‐based evolutionary computations as adaptive dataflows

Complex metaheuristics

Contact Info

Product

Resources

About