Chaos driven discrete artificial bee algorithm for location and assignment optimisation problems

Metlicka, Magdalena; Davendra, Donald

doi:10.1016/j.swevo.2015.03.002

Cited by 43 publications

(11 citation statements)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The observed performance of enhanced optimizer is (significantly) different, mostly the chaotic maps secured very fast progress towards function extreme, but often followed by premature convergence, thus overall statistics has given mixed results. Nevertheless, as reported in [106], the the chaos driven heuristics is performing very well [104,107], especially for some instances in the discrete domain [33,72].…”

Section: Parameter Estimationsupporting

confidence: 55%

Towards Human Cell Simulation

Spolaor

Gribaudo

Iacono

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

The faithful reproduction and accurate prediction of the phenotypes and emergent behaviors of complex cellular systems are among the most challenging goals in Systems Biology. Although mathematical models that describe the interactions among all biochemical processes in a cell are theoretically feasible, their simulation is generally hard because of a variety of reasons. For instance, many quantitative data (e.g., kinetic rates) are usually not available, a problem that hinders the execution of simulation algorithms as long as some parameter estimation methods are used. Though, even with a candidate parameterization, the simulation of mechanistic models could be challenging due to the extreme computational effort required. In this context, model reduction techniques and High-Performance Computing infrastructures could be leveraged to mitigate these issues. In addition, as cellular processes are characterized by multiple scales of temporal and spatial organization, novel hybrid simulators able to harmonize different modeling approaches (e.g., logic-based, constraint-based, continuous deterministic, discrete stochastic, spatial) should be designed. This chapter describes a putative unified approach to tackle these challenging tasks, hopefully paving the way to the definition of large-scale comprehensive models that aim at the comprehension of the cell behavior by means of computational tools.

show abstract

Section: Parameter Estimationsupporting

confidence: 55%

Towards Human Cell Simulation

Spolaor

Gribaudo

Iacono

et al. 2019

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…The chaos system has been mathematically proven to overcome the major drawbacks of classical random number systems. Due to the certainty, ergodicity, and stochastic property, recently, chaotic sequences have been adopted in evolutionary algorithms to avoid local optimization [25,[47][48][49][50][51]. With a chaos search strategy (CST), a population can perform across almost all states in a certain range without duplication.…”

Section: Chaos Searching Strategymentioning

confidence: 99%

A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition

Zhou

Yao

2016

Int J Adv Manuf Technol

130

View full text Add to dashboard Cite

With the advent of cloud manufacturing (CMfg), more and more services in CMfg platforms may provide the same functionality but differ in performance. In order to insure the manufacturing cloud to match the complicated task requirements, composited CMfg service optimal selection (CCSOS) is becoming increasingly important. This study proposes a new approach for such CCSOS problems, the so-called hybrid artificial bee colony (HABC) algorithm, which employs both the probabilistic model of Archimedean copula estimation of distribution algorithm (ACEDA) and the chaos operators of global best-guided artificial bee colony to generate the offspring individuals with consideration of quality of service (QoS) and CMfg environment. Different-scale CCSOS problems are adopted to evaluate the performance of the proposed HABC. Experimental results have shown that the HABC can find better solutions compared with such algorithms as genetic algorithm, particle swarm optimization, and basic artificial bee colony algorithm.

show abstract

“…An example of already done initial analysis of complex network generated by algorithm dynamics, see Figs. 2 and 6, and where [51] basic attributes of complex network are interpreted and reported in the SEA terminology as well as in [130,132] where it was used in EAs performance improvements.…”

Section: Analysis Of Swarm Dynamicsmentioning

confidence: 99%

A survey on evolutionary algorithms dynamics and its complexity – Mutual relations, past, present and future

Zelinka

2015

Swarm and Evolutionary Computation

View full text Add to dashboard Cite

Chaos driven discrete artificial bee algorithm for location and assignment optimisation problems

Cited by 43 publications

References 52 publications

Towards Human Cell Simulation

Towards Human Cell Simulation

A hybrid artificial bee colony algorithm for optimal selection of QoS-based cloud manufacturing service composition

A survey on evolutionary algorithms dynamics and its complexity – Mutual relations, past, present and future

Contact Info

Product

Resources

About