Special Issue "Nature Inspired Optimization Algorithms Recent Advances and Applications"

A special issue of Algorithms (ISSN 1999-4893).

Deadline for manuscript submissions: 31 December 2018

Special Issue Editors

Guest Editor
Dr. Xiao-Zhi Gao

School of Computing, University of Eastern Finland, Finland
Website | E-Mail
Interests: nature-inspired computing methods in optimization; data mining; machine learning
Assistant Guest Editor
Dr. Allouani Fouad

Department of Industrial Engineering, University of Khenchela, Algeria
E-Mail
Interests: evolutionary computation; swarm intelligence; intelligent control systems

Special Issue Information

Dear Colleagues,

Nature-inspired optimization algorithms represent a very important research field in computational intelligence, soft computing, and optimization in a general sense. For this purpose, we observe clearly that they attract outstanding interest from many researchers around the world. Indeed, past and ongoing research in this field cover an important group of subjects, from basic research to a large number of real-world applications in almost all areas, which include science, engineering, industry, economics, and business. The creation of many new algorithms based on natural processes like natural selection, food foraging, physical laws, group movements and other natural models have made this field of research very rich. These algorithms offer very powerful tools to handle these problems, which cannot be solved using traditional and classical mathematical methods, because they not require any mathematical conditions to be satisfied. It should be noted that a general look leads to the finding that nature-inspired algorithms can be generally classified into two main categories: Evolutionary algorithms and swarm intelligence. There are a few algorithms however that do not fall in any of these categories, e.g., gravitational search, harmony search, etc.

The principal aim of this Special Issue is to assemble state-of-the-art contributions on the latest research and development, up-to-date issues, and challenges in the field of nature-inspired optimization algorithms. Proposed submissions should be original, unpublished, and should present novel in-depth fundamental research contributions either from a methodological perspective or from an application point of view. Topics of interest include, but are not only limited to:

Swarm Intelligence (SI)-based algorithms:
Ant Colony Optimization,
Ant Lion Optimization,
Artificial Bee Colony,
Bacterial foraging,
Bacterial-GA Foraging,
Bat Algorithm,
BeeHive,
Bumblebees,
Cat swarm,
Consultant-guided search
Cuckoo Search,
Krill Herd,
Monkey search,
Particle Swarm Optimisation,
Weightless Swarm Algorithm.

Bio-inspired (not SI-based) algorithms:
Atmosphere clouds model,
Biogeography based Optimization,
Brain Storm Optimization,
Differential Evolution,
Dolphin echolocation,
Japanese tree frogs calling,
Eco-inspired evolutionary algorithm,
Egyptian Vulture,
Fish-school Search,
Flower pollination Algorithm,
Firefly Algorithms,
Gene expression.

Physics and Chemistry based algorithms:
Big bang-big Crunch,
Black hole,
Central force optimization,
Charged system search,
Electro-magnetism optimization,
Galaxy-based search algorithm,
Gravitational search,
Harmony Search,
Intelligent water drop,
River formation dynamics,
Self-propelled particles,
Simulated Annealing,
Stochastic diffusion search,
Spiral optimization,
Water cycle algorithm.

Dr. Xiao-Zhi Gao
Dr. Allouani Fouad
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 850 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (1 paper)

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Research

Open AccessArticle Local Coupled Extreme Learning Machine Based on Particle Swarm Optimization
Algorithms 2018, 11(11), 174; https://doi.org/10.3390/a11110174
Received: 20 August 2018 / Revised: 16 October 2018 / Accepted: 29 October 2018 / Published: 1 November 2018
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Abstract
We developed a new method of intelligent optimum strategy for a local coupled extreme learning machine (LC-ELM). In this method, both the weights and biases between the input layer and the hidden layer, as well as the addresses and radiuses in the local
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We developed a new method of intelligent optimum strategy for a local coupled extreme learning machine (LC-ELM). In this method, both the weights and biases between the input layer and the hidden layer, as well as the addresses and radiuses in the local coupled parameters, are determined and optimized based on the particle swarm optimization (PSO) algorithm. Compared with extreme learning machine (ELM), LC-ELM and extreme learning machine based on particle optimization (PSO-ELM) that have the same network size or compact network configuration, simulation results in terms of regression and classification benchmark problems show that the proposed algorithm, which is called LC-PSO-ELM, has improved generalization performance and robustness. Full article
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