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

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

Deadline for manuscript submissions: closed (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 Proposed submissions should be original, unpublished, and should present novel in-depth fundamental research contributions either from a methodological perspective or from an application point of view.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 1000 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 (6 papers)

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Research

Open AccessArticle A Novel Coupling Algorithm Based on Glowworm Swarm Optimization and Bacterial Foraging Algorithm for Solving Multi-Objective Optimization Problems
Algorithms 2019, 12(3), 61; https://doi.org/10.3390/a12030061
Received: 28 December 2018 / Revised: 30 January 2019 / Accepted: 5 March 2019 / Published: 11 March 2019
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Abstract
In the real word, optimization problems in multi-objective optimization (MOP) and dynamic optimization can be seen everywhere. During the last decade, among various swarm intelligence algorithms for multi-objective optimization problems, glowworm swarm optimization (GSO) and bacterial foraging algorithm (BFO) have attracted increasing attention [...] Read more.
In the real word, optimization problems in multi-objective optimization (MOP) and dynamic optimization can be seen everywhere. During the last decade, among various swarm intelligence algorithms for multi-objective optimization problems, glowworm swarm optimization (GSO) and bacterial foraging algorithm (BFO) have attracted increasing attention from scholars. Although many scholars have proposed improvement strategies for GSO and BFO to keep a good balance between convergence and diversity, there are still many problems to be solved carefully. In this paper, a new coupling algorithm based on GSO and BFO (MGSOBFO) is proposed for solving dynamic multi-objective optimization problems (dMOP). MGSOBFO is proposed to achieve a good balance between exploration and exploitation by dividing into two parts. Part I is in charge of exploitation by GSO and Part II is in charge of exploration by BFO. At the same time, the simulation binary crossover (SBX) and polynomial mutation are introduced into the MGSOBFO to enhance the convergence and diversity ability of the algorithm. In order to show the excellent performance of the algorithm, we experimentally compare MGSOBFO with three algorithms on the benchmark function. The results suggests that such a coupling algorithm has good performance and outperforms other algorithms which deal with dMOP. Full article
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Open AccessArticle Autonomous Population Regulation Using a Multi-Agent System in a Prey–Predator Model That Integrates Cellular Automata and the African Buffalo Optimization Metaheuristic
Algorithms 2019, 12(3), 59; https://doi.org/10.3390/a12030059
Received: 1 January 2019 / Revised: 28 February 2019 / Accepted: 1 March 2019 / Published: 6 March 2019
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Abstract
This research focused on the resolution of a dynamic prey–predator spatial model. This model has six life cycles and simulates a theoretical population of prey and predators. Cellular automata represent a set of prey and predators. The cellular automata move in a discrete [...] Read more.
This research focused on the resolution of a dynamic prey–predator spatial model. This model has six life cycles and simulates a theoretical population of prey and predators. Cellular automata represent a set of prey and predators. The cellular automata move in a discrete space in a 2d lattice that has the shape of a torus. African buffaloes represent the predators, and the grasslands represent the prey in the African savanna. Each buffalo moves in the discrete space using the proper motion equation of the African buffalo optimization metaheuristic. Two types of approaches were made with five experiments each. The first approach was the development of a dynamic prey–predator spatial model using the movement of the African buffalo optimization metaheuristic. The second approach added the characteristic of regulating the population of buffaloes using autonomous multi-agents that interact with the model dynamic prey–predator spatial model. According to the obtained results, it was possible to adjust and maintain a balance of prey and predators during a determined period using multi-agents, therefore preventing predators from destroying an entire population of prey in the coexistence space. Full article
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Open AccessArticle Parameter Tuning of PI Control for Speed Regulation of a PMSM Using Bio-Inspired Algorithms
Algorithms 2019, 12(3), 54; https://doi.org/10.3390/a12030054
Received: 31 December 2018 / Revised: 1 February 2019 / Accepted: 9 February 2019 / Published: 4 March 2019
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Abstract
This article focuses on the optimal gain selection for Proportional Integral (PI) controllers comprising a speed control scheme for the Permanent Magnet Synchronous Motor (PMSM). The gains calculation is performed by means of different algorithms inspired by nature, which allows improvement of the [...] Read more.
This article focuses on the optimal gain selection for Proportional Integral (PI) controllers comprising a speed control scheme for the Permanent Magnet Synchronous Motor (PMSM). The gains calculation is performed by means of different algorithms inspired by nature, which allows improvement of the system performance in speed regulation tasks. For the tuning of the control parameters, five optimization algorithms are chosen: Bat Algorithm (BA), Biogeography-Based Optimization (BBO), Cuckoo Search Algorithm (CSA), Flower Pollination Algorithm (FPA) and Sine-Cosine Algorithm (SCA). Finally, for purposes of efficiency assessment, two reference speed profiles are introduced, where an acceptable PMSM performance is attained by using the proposed PI controllers tuned by nature inspired algorithms. Full article
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Open AccessArticle Comparative Study in Fuzzy Controller Optimization Using Bee Colony, Differential Evolution, and Harmony Search Algorithms
Algorithms 2019, 12(1), 9; https://doi.org/10.3390/a12010009
Received: 2 December 2018 / Revised: 19 December 2018 / Accepted: 23 December 2018 / Published: 27 December 2018
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Abstract
This paper presents a comparison among the bee colony optimization (BCO), differential evolution (DE), and harmony search (HS) algorithms. In addition, for each algorithm, a type-1 fuzzy logic system (T1FLS) for the dynamic modification of the main parameters is presented. The dynamic adjustment [...] Read more.
This paper presents a comparison among the bee colony optimization (BCO), differential evolution (DE), and harmony search (HS) algorithms. In addition, for each algorithm, a type-1 fuzzy logic system (T1FLS) for the dynamic modification of the main parameters is presented. The dynamic adjustment in the main parameters for each algorithm with the implementation of fuzzy systems aims at enhancing the performance of the corresponding algorithms. Each algorithm (modified and original versions) is analyzed and compared based on the optimal design of fuzzy systems for benchmark control problems, especially in fuzzy controller design. Simulation results provide evidence that the FDE algorithm outperforms the results of the FBCO and FHS algorithms in the optimization of fuzzy controllers. Statistically is demonstrated that the better errors are found with the implementation of the fuzzy systems to enhance each proposed algorithm. Full article
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Open AccessArticle Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning
Algorithms 2019, 12(1), 3; https://doi.org/10.3390/a12010003
Received: 23 November 2018 / Revised: 17 December 2018 / Accepted: 20 December 2018 / Published: 21 December 2018
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Abstract
Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (QPIO) has been widely applied to such nonlinear problems. However, [...] Read more.
Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (QPIO) has been widely applied to such nonlinear problems. However, conventional QPIO is suffering low global convergence speed and local optimum. In order to solve the above problems, an improved QPIO algorithm, adaptive operator QPIO, is proposed in this paper. Firstly, a new initialization process based on logistic mapping method is introduced to generate the initial population of the pigeon-swarm. After that, to improve the performance of the map and compass operation, the factor parameter will be adaptively updated in each iteration, which can balance the ability between global and local search. In the final landmark operation, the gradual decreasing pigeon population-updating strategy is introduced to prevent premature convergence and local optimum. Finally, the demonstration of the proposed algorithm on UAV path planning problem is presented, and the comparison result indicates that the performance of our algorithm is better than that of particle swarm optimization (PSO), pigeon-inspired optimization (PIO), and its variants, in terms of convergence and accuracy. Full article
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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 [...] Read more.
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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