Krill herd: A new bio-inspired optimization algorithm

Gandomi, Amir H.; Alavi, Amir H.

doi:10.1016/j.cnsns.2012.05.010

Cited by 1,685 publications

(785 citation statements)

References 23 publications

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“…The algorithm's parameters change over time following a chaotic mapping in order to have a robust convergence strategy. The convergence of this simplified PSO has been proved, although it may suffer in highly non-linear multi-modal problems [48]. Here, the sinusoidal chaotic mapping proposed in [47] is used for updating the position equation parameters.…”

Section: Particle Swarm Optimisation (Pso)mentioning

confidence: 99%

Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

Poole

Kotsialos

2016

Applied Soft Computing

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Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-pro t purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. AbstractThis paper is concerned with the problem of macroscopic road traffic flow model calibration and verification. Thoroughly validated models are necessary for both control system design and scenario evaluation purposes. Here, the second order traffic flow model METANET was calibrated and verified using real data.A powerful optimisation problem formulation is proposed for identifying a set of model parameters that makes the model fit to measurements. For the macroscopic traffic flow model validation problem, this set of parameters characterise the aggregate traffic flow features over a road network. In traffic engineering, one of the most important relationships whose parameters need to be determined is the fundamental diagram of traffic, which models the non-linear relationship between vehicular flow and density. Typically, a real network does not exhibit the same traffic flow aggregate behaviour everywhere and different fundamental diagrams are used for covering different network areas. As a result, one of the initial steps of the validation process rests on expert engineering opinion assigning the spatial extension of fundamental diagrams. The proposed optimisation problem formulation allows for automatically determining the number of different fundamental diagrams to be used and their corresponding spatial extension over the road network, simplifying this initial step. Although the optimisation problem suffers from local minima, good solutions which generalise well were obtained.The design of the system used is highly generic and allows for a number of evolutionary and swarm intelligence algorithms to be used. Two UK sites have been used for testing it. Calibration and verification results are Preprint submitted to Applied Soft Computing September 18, 2015 discussed in detail. The resulting models are able to capture the dynamics of traffic flow and replicate shockwave propagation. A total of ten different algorithms were considered and compared with respect to their ability to converge to a solution, which remains valid for different sets of data. Particle Swarm Optimisation (PSO) algorithms have proven to be particularly effective and provide the best results both in terms of speed of convergence and solution generalisation. An interesting result reported is that more recently proposed PSO algorithms were outperformed by older variants, both in terms of speed of convergence and model error minimisation.

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Section: Particle Swarm Optimisation (Pso)mentioning

confidence: 99%

Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

Poole

Kotsialos

2016

Applied Soft Computing

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“…The foraging behavior of ants as in ant colony optimization (ACO) [13], [19], [20], the choreography of bird flocks as in the partial swarm optimization (PSO) [21], the intelligent behavior of honey bee swarms as in an artificial bee colony (ABC) [22], [23] are examples that inspired from animal collective life. In addition, the gravitational search algorithm (GSA) [15], [24] is based on the mass interactions and the law of gravity, Krill herd algorithm (KHA) [25] is inspired from herding behavior of krill individuals, and Intelligent water drops (IWD) [26] are simulated natural rivers and how they find almost optimal paths to their destination. Onthe rest of paper the first-fourth algorithms as most popular will be explained and reviewed their application in GrSC.…”

Section: Swarm Based Algorithmsmentioning

confidence: 99%

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

Sadrnia¹,

Soltani²,

Zulkifli³

et al. 2014

IJET

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Abstract-In the recent years, there is a significant attention among researchers and practitioners to environmental issues and green supply chain (GrSC) because of legislations and profit oriented motivations.Because of considering environmental issues GrSC besides the economic variables, the models are very difficult to find optimal. Hence, complicated green problems call for development of modern optimization methods and algorithms to solve optimization models by efficient techniques. In recent decades, meta-heuristic algorithms have been developed to overcome the problem that most of them are inspired from nature. Some of the algorithms have been inspired from natural generation, some of them inspired from swarm behavior, and others simulate natural processes. In this research we summarize the recent advance evolutionary optimization algorithms and swarm intelligence algorithms which are applied to GrSC and green logistics. Literature reviewed in this paper shows the current state of the art and discusses the potential future research trends.Index Terms-Evolutionary algorithms, green supply chain, meta-heuristic, swarm intelligence algorithms.

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“…In 2016, an additional update equation [21] for all ABC-based optimization algorithms was developed to speed up the convergence utilizing Bollinger bands [22] which is a technical analysis tool to predict maximum or minimum future stock prices. Wang et al [23] proposed a hybridization method based on krill herd [24] and ABC (KHABC) in 2017. A neighbor food source for onlooker bees in ABC was obtained from the global optimal solutions, which was found by the KHABC algorithm.…”

Section: Introductionmentioning

confidence: 99%

A Developed Artificial Bee Colony Algorithm Based on Cloud Model

Jin

Sun

2018

Mathematics

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Abstract:The Artificial Bee Colony (ABC) algorithm is a bionic intelligent optimization method. The cloud model is a kind of uncertainty conversion model between a qualitative concept T that is presented by nature language and its quantitative expression, which integrates probability theory and the fuzzy mathematics. A developed ABC algorithm based on cloud model is proposed to enhance accuracy of the basic ABC algorithm and avoid getting trapped into local optima by introducing a new select mechanism, replacing the onlooker bees' search formula and changing the scout bees' updating formula. Experiments on CEC15 show that the new algorithm has a faster convergence speed and higher accuracy than the basic ABC and some cloud model based ABC variants.

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Krill herd: A new bio-inspired optimization algorithm

Cited by 1,685 publications

References 23 publications

Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

Swarm intelligence algorithms for macroscopic traffic flow model validation with automatic assignment of fundamental diagrams

A Review of Nature-Based Algorithms Applications in Green Supply Chain Problems

A Developed Artificial Bee Colony Algorithm Based on Cloud Model

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