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Algorithms
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Bio-inspired Algorithms for Combinatorial Problems
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Bio-inspired Algorithms for Combinatorial Problems

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

Deadline for manuscript submissions: closed (31 March 2014) | Viewed by 45390

Special Issue Editors

Prof. Dr. Ruay-Shiung Chang

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Dong Hwa University, No. 1, Section 2, Dashieh Road, Shoufeng, Hualien, Taiwan
Interests: wireless networks; sensor networks; internet; cloud computing
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Sheng-Lung Peng

E-Mail Website
Guest Editor
Department of Computer Science and Information Engineering, National Dong Hwa University, No. 1, Section 2, Dashieh Road, Shoufeng, Hualien, Taiwan
Interests: graph algorithms; bioinformatics

Special Issue Information

Dear Colleagues,

There is evidence that nature is a great source for inspirations to both develop intelligent systems and provide solutions to complicated problems. Taking animals for an example, evolutionary pressure forces them to develop highly optimized organs and skills for their survival. Some of the organs and their behaviors can be learned to design algorithms for real-world problems. For example, it is interesting to know how a group of ants can locate a relatively short path between their food in a distant place and their nets. Of course we now know that a substance called pheromone that ants emit play an important role. But can computer simulate ants' behavior and use this path finding technique to solve problems in other disciplines? Ant Colony Optimization (ACO) algorithm (abbreviated Ant Algorithm) is the research that pursues problem solution in this direction. Similar cases are Bees Algorithms (BA), Genetic Algorithms (GA), Firefly Algorithms (FA) and so on.
In this special issue of "Bio-inspired Algorithms for Combinatorial Problems", we seek original research or practical application results in the area of bio-inspired algorithms for combinatorial problems.

Acceptable topics include, but are not limited to, the following:

  • Design and analysis of ACO algorithms
  • Design and analysis of Bees algorithms
  • Design and analysis of Firefly algorithms
  • Design and analysis of Genetic algorithms
  • Other swarm intelligence algorithms
  • Practical real-life applications of Bio-inspired algorithms
  • Limitations of Bio-inspired algorithms
  • Applications to NP-complete problems

Prof. Dr. Ruay-Shiung Chang
Prof. Dr. Sheng-Lung Peng
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 submissions that pass pre-check are 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 1600 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.

Keywords

  • ant algorithm
  • combinatorial optimization algorithm
  • swarm intelligence
  • ant colony
  • ant heuristics

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Published Papers (6 papers)

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Research

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224 KiB  
Article
Target Channel Visiting Order Design Using Particle Swarm Optimization for Spectrum Handoff in Cognitive Radio Networks
by Shilian Zheng, Zhijin Zhao, Changlin Luo and Xiaoniu Yang
Algorithms 2014, 7(3), 418-428; https://doi.org/10.3390/a7030418 - 18 Aug 2014
Cited by 2 | Viewed by 5409
Abstract
In a dynamic spectrum access network, when a primary user (licensed user) reappears on the current channel, cognitive radios (CRs) need to vacate the channel and reestablish a communications link on some other channel to avoid interference to primary users, resulting in spectrum [...] Read more.
In a dynamic spectrum access network, when a primary user (licensed user) reappears on the current channel, cognitive radios (CRs) need to vacate the channel and reestablish a communications link on some other channel to avoid interference to primary users, resulting in spectrum handoff. This paper studies the problem of designing target channel visiting order for spectrum handoff to minimize expected spectrum handoff delay. A particle swarm optimization (PSO) based algorithm is proposed to solve the problem. Simulation results show that the proposed algorithm performs far better than random target channel visiting scheme. The solutions obtained by PSO are very close to the optimal solution which further validates the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Bio-inspired Algorithms for Combinatorial Problems)
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276 KiB  
Article
A Hybrid Metaheuristic Approach for Minimizing the Total Flow Time in A Flow Shop Sequence Dependent Group Scheduling Problem
by Antonio Costa, Fulvio Antonio Cappadonna and Sergio Fichera
Algorithms 2014, 7(3), 376-396; https://doi.org/10.3390/a7030376 - 14 Jul 2014
Cited by 12 | Viewed by 6458
Abstract
Production processes in Cellular Manufacturing Systems (CMS) often involve groups of parts sharing the same technological requirements in terms of tooling and setup. The issue of scheduling such parts through a flow-shop production layout is known as the Flow-Shop Group Scheduling (FSGS) problem [...] Read more.
Production processes in Cellular Manufacturing Systems (CMS) often involve groups of parts sharing the same technological requirements in terms of tooling and setup. The issue of scheduling such parts through a flow-shop production layout is known as the Flow-Shop Group Scheduling (FSGS) problem or, whether setup times are sequence-dependent, the Flow-Shop Sequence-Dependent Group Scheduling (FSDGS) problem. This paper addresses the FSDGS issue, proposing a hybrid metaheuristic procedure integrating features from Genetic Algorithms (GAs) and Biased Random Sampling (BRS) search techniques with the aim of minimizing the total flow time, i.e., the sum of completion times of all jobs. A well-known benchmark of test cases, entailing problems with two, three, and six machines, is employed for both tuning the relevant parameters of the developed procedure and assessing its performances against two metaheuristic algorithms recently presented by literature. The obtained results and a properly arranged ANOVA analysis highlight the superiority of the proposed approach in tackling the scheduling problem under investigation. Full article
(This article belongs to the Special Issue Bio-inspired Algorithms for Combinatorial Problems)
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503 KiB  
Article
Solving the Examination Timetabling Problem in GPUs
by Vasileios Kolonias, George Goulas, Christos Gogos, Panayiotis Alefragis and Efthymios Housos
Algorithms 2014, 7(3), 295-327; https://doi.org/10.3390/a7030295 - 3 Jul 2014
Cited by 5 | Viewed by 8109
Abstract
The examination timetabling problem belongs to the class of combinatorial optimization problems and is of great importance for every University. In this paper, a hybrid evolutionary algorithm running on a GPU is employed to solve the examination timetabling problem. The hybrid evolutionary algorithm [...] Read more.
The examination timetabling problem belongs to the class of combinatorial optimization problems and is of great importance for every University. In this paper, a hybrid evolutionary algorithm running on a GPU is employed to solve the examination timetabling problem. The hybrid evolutionary algorithm proposed has a genetic algorithm component and a greedy steepest descent component. The GPU computational capabilities allow the use of very large population sizes, leading to a more thorough exploration of the problem solution space. The GPU implementation, depending on the size of the problem, is up to twenty six times faster than the identical single-threaded CPU implementation of the algorithm. The algorithm is evaluated with the well known Toronto datasets and compares well with the best results found in the bibliography. Moreover, the selection of the encoding of the chromosomes and the tournament selection size as the population grows are examined and optimized. The compressed sparse row format is used for the conflict matrix and was proven essential to the process, since most of the datasets have a small conflict density, which translates into an extremely sparse matrix. Full article
(This article belongs to the Special Issue Bio-inspired Algorithms for Combinatorial Problems)
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265 KiB  
Article
Application of Imperialist Competitive Algorithm on Solving the Traveling Salesman Problem
by Shuhui Xu, Yong Wang and Aiqin Huang
Algorithms 2014, 7(2), 229-242; https://doi.org/10.3390/a7020229 - 13 May 2014
Cited by 21 | Viewed by 7631
Abstract
The imperialist competitive algorithm (ICA) is a new heuristic algorithm proposed for continuous optimization problems. The research about its application on solving the traveling salesman problem (TSP) is still very limited. Aiming to explore its ability on solving TSP, we present a discrete [...] Read more.
The imperialist competitive algorithm (ICA) is a new heuristic algorithm proposed for continuous optimization problems. The research about its application on solving the traveling salesman problem (TSP) is still very limited. Aiming to explore its ability on solving TSP, we present a discrete imperialist competitive algorithm in this paper. The proposed algorithm modifies the original rules of the assimilation and introduces the 2-opt algorithm into the revolution process. To examine its performance, we tested the proposed algorithm on 10 small-scale and 2 large-scale standard benchmark instances from the TSPLIB and compared the experimental results with that obtained by two other ICA-based algorithms and six other existing algorithms. The proposed algorithm shows excellent performance in the experiments and comparisons. Full article
(This article belongs to the Special Issue Bio-inspired Algorithms for Combinatorial Problems)
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317 KiB  
Article
Stochastic Diffusion Search: A Comparison of Swarm Intelligence Parameter Estimation Algorithms with RANSAC
by Howard Williams and Mark Bishop
Algorithms 2014, 7(2), 206-228; https://doi.org/10.3390/a7020206 - 5 May 2014
Cited by 13 | Viewed by 9292
Abstract
Stochastic diffusion search (SDS) is a multi-agent global optimisation technique based on the behaviour of ants, rooted in the partial evaluation of an objective function and direct communication between agents. Standard SDS, the fundamental algorithm at work in all SDS processes, is presented [...] Read more.
Stochastic diffusion search (SDS) is a multi-agent global optimisation technique based on the behaviour of ants, rooted in the partial evaluation of an objective function and direct communication between agents. Standard SDS, the fundamental algorithm at work in all SDS processes, is presented here. Parameter estimation is the task of suitably fitting a model to given data; some form of parameter estimation is a key element of many computer vision processes. Here, the task of hyperplane estimation in many dimensions is investigated. Following RANSAC (random sample consensus), a widely used optimisation technique and a standard technique for many parameter estimation problems, increasingly sophisticated data-driven forms of SDS are developed. The performance of these SDS algorithms and RANSAC is analysed and compared for a hyperplane estimation task. SDS is shown to perform similarly to RANSAC, with potential for tuning to particular search problems for improved results. Full article
(This article belongs to the Special Issue Bio-inspired Algorithms for Combinatorial Problems)
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Review

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239 KiB  
Review
A Review of Routing Protocols Based on Ant-Like Mobile Agents
by Yasushi Kambayashi
Algorithms 2013, 6(3), 442-456; https://doi.org/10.3390/a6030442 - 6 Aug 2013
Cited by 8 | Viewed by 7194
Abstract
A survey on the routing protocols based on ant-like mobile agents is given. These protocols are often employed in Mobile Ad Hoc Networks (MANET). Mobile Ad Hoc Networks are collections of wireless mobile nodes such as PDAs, laptop computers, and cellular phones having [...] Read more.
A survey on the routing protocols based on ant-like mobile agents is given. These protocols are often employed in Mobile Ad Hoc Networks (MANET). Mobile Ad Hoc Networks are collections of wireless mobile nodes such as PDAs, laptop computers, and cellular phones having wireless communication capability that dynamically form a temporary network without using any existing network infrastructures such as wireless access points. The only infrastructure in MANET is the wireless communication interfaces on the devices. In such a circumstance, where some of the wireless devices are not within wireless range of each other, multi-hop routing is required to transmit messages to the destination. A node that wants to start communication with other nodes that are not within its one-hop wireless transmission range has to request intermediate nodes to forward their communication packets to the destination. In this paper, we survey a variety of proposed network protocols to accommodate this situation. We focus especially on biologically-inspired routing algorithms that are based on the ant colony optimization algorithm. Full article
(This article belongs to the Special Issue Bio-inspired Algorithms for Combinatorial Problems)
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