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A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "Computational and Applied Mathematics".

Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 28803

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Prof. Dr. Linqiang Pan

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School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, China
Interests: biocomputing; artificial intelligence
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Prof. Dr. Zhihua Cui

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Complex System and Computational Intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan 030024, Shanxi, China
Interests: cloud computing; network security; big data modeling and optimization
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Dr. Harish Garg

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School of Mathematics, Thapar Institute of Engineering & Technology, Patiala 147004, Punjab, India
Interests: multicriteria decision making; decision support systems; metaheuristic-based optimization; soft computing; reliability and risk analysis; rough set theory; hesitant set; soft set theory
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Prof. Dr. Thomas Hanne

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Institute for Information Systems, University of Applied Sciences and Arts Northwestern Switzerland, 4142 Münchenstein, Switzerland
Interests: computational intelligence; optimization simulation; multicriteria decision analysis; logistics and supply chain management; evolutionary computation
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Dr. Gai-Ge Wang

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Department of Computer Science and Technology, Ocean University of China, Qingdao 266100, China
Interests: swarm intelligence; evolutionary computation; big data optimization
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Special Issue Information

Dear Colleagues,

BIC, short for biologically inspired computing, is a field of study that loosely combines the related subfields of connectionism, social behavior, and emergence. It is often closely related to the field of artificial intelligence, as many of its goals can be linked to machine learning. It is also closely related to the fields of biology, computer science and mathematics. In short, it is the use of computers to simulate the phenomena of life and to improve the use of computers by studying living things. Biologically inspired computing is a major subset of natural computing. Biologically inspired computing is different from traditional artificial intelligence (AI) in that it uses a more evolved learning method instead of the so-called "creation theory" method in traditional artificial intelligence.

The purpose of this Special Issue is to gather a collection of articles that cover the latest developments in different fields of biologically inspired computing, evolutionary algorithms, biodegradability prediction, cellular automaton, the neural network, and others.

Prof. Dr. Linqiang Pan
Prof. Dr. Zhihua Cui
Dr. Harish Garg
Prof. Dr. Thomas Hanne
Prof. Dr. Gai-Ge Wang
Guest Editors

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 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

Biologically inspired computing
Evolutionary algorithms
Biodegradability prediction
Cellular automaton
The neural network
Artificial immune system

Published Papers (10 papers)

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Research

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21 pages, 720 KiB

Open AccessArticle

A Two-State Dynamic Decomposition-Based Evolutionary Algorithm for Handling Many-Objective Optimization Problems

by Lining Xing, Jun Li, Zhaoquan Cai and Feng Hou

Mathematics 2023, 11(3), 493; https://doi.org/10.3390/math11030493 - 17 Jan 2023

Viewed by 1392

Abstract

Decomposition-based many-objective evolutionary algorithms (D-MaOEAs) are brilliant at keeping population diversity for predefined reference vectors or points. However, studies indicate that the performance of an D-MaOEA strongly depends on the similarity between the shape of the reference vectors (points) and that of the PF (a set of Pareto-optimal solutions symbolizing balance among objectives of many-objective optimization problems) of the many-objective problem (MaOP). Generally, MaOPs with expected PFs are not realistic. Consequently, the inevitable weak similarity results in many inactive subspaces, creating huge difficulties for maintaining diversity. To address these issues, we propose a two-state method to judge the decomposition status according to the number of inactive reference vectors. Then, two novel reference vector adjustment strategies, set as parts of the environmental selection approach, are tailored for the two states to delete inactive reference vectors and add new active reference vectors, respectively, in order to ensure that the reference vectors are as close as possible to the PF of the optimization problem. Based on the above strategies and an efficient convergence performance indicator, an active reference vector-based two-state dynamic decomposition-base MaOEA, referred to as ART-DMaOEA, is developed in this paper. Extensive experiments were conducted on ART-DMaOEA and five state-of-the-art MaOEAs on MaF1-MaF9 and WFG1-WFG9, and the comparative results show that ART-DMaOEA has the most competitive overall performance. Full article

(This article belongs to the Special Issue Biologically Inspired Computing)

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19 pages, 1085 KiB

Open AccessArticle

Knowledge-Based Evolutionary Optimizing Makespan and Cost for Cloud Workflows

by Lining Xing, Rui Wu, Jiaxing Chen and Jun Li

Mathematics 2023, 11(1), 38; https://doi.org/10.3390/math11010038 - 22 Dec 2022

Viewed by 1105

Abstract

Workflow scheduling is essential to simultaneously optimize the makespan and economic cost for cloud services and has attracted intensive interest. Most of the existing multi-objective cloud workflow scheduling algorithms regard the focused problems as black-boxes and design evolutionary operators to perform random searches, which are inefficient in dealing with the elasticity and heterogeneity of cloud resources as well as complex workflow structures. This study explores the characteristics of cloud resources and workflow structures to design a knowledge-based evolutionary optimization operator, named KEOO, with two novel features. First, we develop a task consolidation mechanism to reduce the number of cloud resources used, reducing the economic cost of workflow execution without delaying its finish time. Then, we develop a critical task adjustment mechanism to selectively move the critical predecessors of some tasks to the same resources to eliminate the data transmission overhead between them, striving to improve the economic cost and finish time simultaneously. At last, we embed the proposed KEOO into four classical multi-objective algorithms, i.e., NSGA-II, HypE, MOEA/D, and RVEA, forming four variants: KEOO-NSGA-II, KEOO-HypE, KEOO-MOEA/D, and KEOO-RVEA, for comparative experiments. The comparison results demonstrate the effectiveness of the KEOO in improving these four algorithms in solving cloud workflow scheduling problems. Full article

(This article belongs to the Special Issue Biologically Inspired Computing)

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34 pages, 5877 KiB

Open AccessArticle

Combining Key-Points-Based Transfer Learning and Hybrid Prediction Strategies for Dynamic Multi-Objective Optimization

by Yong Wang, Kuichao Li and Gai-Ge Wang

Mathematics 2022, 10(12), 2117; https://doi.org/10.3390/math10122117 - 17 Jun 2022

Cited by 7 | Viewed by 1371

Abstract

Dynamic multi-objective optimization problems (DMOPs) have been of interest to many researchers. These are problems in which the environment changes during the evolutionary process, such as the Pareto-optimal set (POS) or the Pareto-optimal front (POF). This kind of problem imposes more challenges and difficulties for evolutionary algorithms, mainly because it demands population to track the changing POF efficiently and accurately. In this paper, we propose a new approach combining key-points-based transfer learning and hybrid prediction strategies (KPTHP). In particular, the transfer process combines predictive strategy with obtaining anticipated key points depending on the previous moments to acquire the optimal individuals at the new instance during the evolution. Additionally, center-point-based prediction is used to complement transfer learning to comprehensively generate initial populations. KPTHP and six state-of-the-art algorithms are tested on various test functions for MIGD, DMIGD, MMS, and HVD metrics. KPTHP obtains superior results on most of the tested functions, which shows that our algorithm performs excellently in both convergence and diversity, with more competitiveness in addressing dynamic problems. Full article

(This article belongs to the Special Issue Biologically Inspired Computing)

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15 pages, 976 KiB

Open AccessArticle

Research on Formation Control Method of Heterogeneous AUV Group under Event-Triggered Mechanism

by Ke Chen, Guangyu Luo, Hao Zhou and Dongming Zhao

Mathematics 2022, 10(9), 1373; https://doi.org/10.3390/math10091373 - 20 Apr 2022

Cited by 5 | Viewed by 1371

Abstract

The time-sampling control strategy has communication discontinuities in the control of multiple AUVs (autonomous underwater vehicles). To overcome this problem, a distributed event-triggered communication mechanism is proposed to make each AUV communicate only when its own state is updated, which reduces the frequency of communication and improves the stability. This mechanism has better adaptability for formation control between heterogeneous AUV groups. At the same time, two consistency control algorithms based on event-triggered for homogeneous and heterogeneous AUV groups are studied, respectively. The known consistency algorithms are applied to the control of heterogeneous AUV groups for comparative analysis. The simulation results demonstrate that the number of communication among AUVs under the event-triggered control strategy can be significantly reduced. Therefore, the stability of the system is improved. Compared with the traditional consensus algorithm, the algorithm proposed in this paper has advantages in the control of heterogeneous AUV groups. Full article

(This article belongs to the Special Issue Biologically Inspired Computing)

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21 pages, 640 KiB

Open AccessArticle

stigLD: Stigmergic Coordination in Linked Systems

by René Schubotz, Torsten Spieldenner and Melvin Chelli

Mathematics 2022, 10(7), 1041; https://doi.org/10.3390/math10071041 - 24 Mar 2022

Cited by 1 | Viewed by 1413

Abstract

While current Semantic Web technologies are well-suited for data publication and integration, the design and deployment of dynamic, autonomous and long-lived multi-agent systems (MAS) on the Web is still in its infancy. Following the vision of hypermedia MAS and Linked Systems, we propose to use a value-passing fragment of Milner’s Calculus to formally specify the generic hypermedia-driven behaviour of Linked Data agents and the Web as their embedding environment. We are specifically interested in agent coordination mechanisms based on stigmergic principles. When considering transient marker-based stigmergy, we identify the necessity of generating server-side effects during the handling of safe and idempotent agent-initiated resource requests. This design choice is oftentimes contested with an imprecise interpretation of HTTP semantics, or with rejecting environments as first-class abstractions in MAS. Based on our observations, we present a domain model and a SPARQL function library facilitating the design and implementation of stigmergic coordination between Linked Data agents on the Web. We demonstrate the efficacy our of modelling approach in a Make-to-Order fulfilment scenario involving transient stigmergy and negative feedback as well as by solving a problem instance from the (time constrained) Trucks World domain as presented in the fifth International Planning Competition. Full article

(This article belongs to the Special Issue Biologically Inspired Computing)

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21 pages, 1639 KiB

Open AccessArticle

Bio-Constrained Codes with Neural Network for Density-Based DNA Data Storage

by Abdur Rasool, Qiang Qu, Yang Wang and Qingshan Jiang

Mathematics 2022, 10(5), 845; https://doi.org/10.3390/math10050845 - 07 Mar 2022

Cited by 16 | Viewed by 3029

Abstract

DNA has evolved as a cutting-edge medium for digital information storage due to its extremely high density and durable preservation to accommodate the data explosion. However, the strings of DNA are prone to errors during the hybridization process. In addition, DNA synthesis and sequences come with a cost that depends on the number of nucleotides present. An efficient model to store a large amount of data in a small number of nucleotides is essential, and it must control the hybridization errors among the base pairs. In this paper, a novel computational model is presented to design large DNA libraries of oligonucleotides. It is established by integrating a neural network (NN) with combinatorial biological constraints, including constant GC-content and satisfying Hamming distance and reverse-complement constraints. We develop a simple and efficient implementation of NNs to produce the optimal DNA codes, which opens the door to applying neural networks for DNA-based data storage. Further, the combinatorial bio-constraints are introduced to improve the lower bounds and to avoid the occurrence of errors in the DNA codes. Our goal is to compute large DNA codes in shorter sequences, which should avoid non-specific hybridization errors by satisfying the bio-constrained coding. The proposed model yields a significant improvement in the DNA library by explicitly constructing larger codes than the prior published codes. Full article

(This article belongs to the Special Issue Biologically Inspired Computing)

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23 pages, 2618 KiB

Open AccessArticle

Self-Adaptive Constrained Multi-Objective Differential Evolution Algorithm Based on the State–Action–Reward–State–Action Method

by Qingqing Liu, Caixia Cui and Qinqin Fan

Mathematics 2022, 10(5), 813; https://doi.org/10.3390/math10050813 - 03 Mar 2022

Cited by 7 | Viewed by 1983

Abstract

The performance of constrained multi-objective differential evolution algorithms (CMOEAs) is mainly determined by constraint handling techniques (CHTs) and their generation strategies. To realize the adaptive adjustment of CHTs and generation strategies, an adaptive constrained multi-objective differential evolution algorithm based on the state–action–reward–state–action (SARSA) approach (ACMODE) is introduced in the current study. In the proposed algorithm, the suitable CHT and the appropriate generation strategy can be automatically selected via a SARSA method. The performance of the proposed algorithm is compared with four other famous CMOEAs on five test suites. Experimental results show that the overall performance of the ACMODE is the best among all competitors, and the proposed algorithm is capable of selecting an appropriate CHT and a suitable generation strategy to solve a particular type of constrained multi-objective optimization problems. Full article

(This article belongs to the Special Issue Biologically Inspired Computing)

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24 pages, 1082 KiB

Open AccessArticle

SPGD: Search Party Gradient Descent Algorithm, a Simple Gradient-Based Parallel Algorithm for Bound-Constrained Optimization

by A. S. Syed Shahul Hameed and Narendran Rajagopalan

Mathematics 2022, 10(5), 800; https://doi.org/10.3390/math10050800 - 02 Mar 2022

Cited by 6 | Viewed by 4767

Abstract

Nature-inspired metaheuristic algorithms remain a strong trend in optimization. Human-inspired optimization algorithms should be more intuitive and relatable. This paper proposes a novel optimization algorithm inspired by a human search party. We hypothesize the behavioral model of a search party searching for a treasure. Motivated by the search party’s behavior, we abstract the “Divide, Conquer, Assemble” (DCA) approach. The DCA approach allows us to parallelize the traditional gradient descent algorithm in a strikingly simple manner. Essentially, multiple gradient descent instances with different learning rates are run parallelly, periodically sharing information. We call it the search party gradient descent (SPGD) algorithm. Experiments performed on a diverse set of classical benchmark functions show that our algorithm is good at optimizing. We believe our algorithm’s apparent lack of complexity will equip researchers to solve problems efficiently. We compare the proposed algorithm with SciPy’s optimize library and it is found to be competent with it. Full article

(This article belongs to the Special Issue Biologically Inspired Computing)

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Review

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19 pages, 1660 KiB

Open AccessReview

Moth Search: Variants, Hybrids, and Applications

by Juan Li, Yuan-Hua Yang, Qing An, Hong Lei, Qian Deng and Gai-Ge Wang

Mathematics 2022, 10(21), 4162; https://doi.org/10.3390/math10214162 - 07 Nov 2022

Cited by 4 | Viewed by 1403

Abstract

Moth search (MS) is a nature-inspired metaheuristic optimization algorithm based on the most representative characteristics of moths, Lévy flights and phototaxis. Phototaxis signifies a movement which organism towards or away from a source of light, which is the representative features for moths. The best moth individual is seen as the light source in Moth search. The moths that have a smaller distance from the best one will fly around the best individual by Lévy flights. For reasons of phototaxis, the moths, far from the fittest one, will fly towards the best one with a big step. These two features, Lévy flights and phototaxis, correspond to the processes of exploitation and exploration for metaheuristic optimization. The superiority of the moth search has been demonstrated in many benchmark problems and various application areas. A comprehensive survey of the moth search was conducted in this paper, which included the three sections: statistical research studies about moth search, different variants of moth search, and engineering optimization/applications. The future insights and development direction in the area of moth search are also discussed. Full article

(This article belongs to the Special Issue Biologically Inspired Computing)

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15 pages, 355 KiB

Open AccessReview

Consistency Indices in Analytic Hierarchy Process: A Review

by Sangeeta Pant, Anuj Kumar, Mangey Ram, Yury Klochkov and Hitesh Kumar Sharma

Mathematics 2022, 10(8), 1206; https://doi.org/10.3390/math10081206 - 07 Apr 2022

Cited by 62 | Viewed by 8456

Abstract

A well-regarded as well as powerful method named the ‘analytic hierarchy process’ (AHP) uses mathematics and psychology for making and analysing complex decisions. This article aims to present a brief review of the consistency measure of the judgments in AHP. Judgments should not be random or illogical. Several researchers have developed different consistency measures to identify the rationality of judgments. This article summarises the consistency measures which have been proposed so far in the literature. Moreover, this paper describes briefly the functional relationships established in the literature among the well-known consistency indices. At last, some thoughtful research directions that can be helpful in further research to develop and improve the performance of AHP are provided as well. Full article

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