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A modified bats echolocation-based algorithm for solving constrained optimisation problems

INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2017年第1期10卷 12-23页

作者： Yahya, Nafrizuan Mat Tokhi, M. Osman Univ Malaysia Pahang Fac Mfg Engn Pahang Malaysia Univ Sheffield Dept Automat Control & Syst Engn Sheffield S Yorkshire England

A modified adaptive bats sonar algorithm (MABSA) is presented that utilises the concept of echolocation of a colony of bats to find prey. The proposed algorithm is applied to solve the constrained optimisation problems coupled with penalty function method as constraint handling technique. The performance of the algorithm is verified through rigorous tests with four constrained optimisation benchmark test functions. The acquired results show that the proposed algorithm performs better to find optimum solution in terms of accuracy and convergence speed. The statistical results of MABSA to solve all the test functions also has been compared with the results from several existing algorithms taken from literature on similar test functions. The comparative study has shown that MABSA outperforms other establish algorithms, and thus, it can be an efficient alternative method in the solving constrained optimisation problems.

关键词： modified adaptive bats sonar algorithm MABSA bats echolocation constrained optimisation problems

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Solving constrained optimisation problems using the improved imperialist competitive algorithm and Deb's technique

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JOURNAL OF EXPERIMENTAL & THEORETICAL ARTIFICIAL INTELLIGENCE 2018年第6期30卷 927-951页

作者： Aliniya, Zahra Keyvanpour, MohammadReza Alzahra Univ Dept Comp Engn Tehran Iran

In this paper, an improved imperialist competitive algorithm called I-ICA is proposed for solving constrained optimisation problems. In I-ICA, the use of differential evolution (DE)/rand/1 mutation operator at assimilation step enhances the population diversity. Also, the binomial crossover operator improves the speed of convergence to optimal solution by distributing good information among solutions. Furthermore, Deb's rules were applied for handling constrains. Examinations were done on 24 well-known functions and 5 engineering design problems. The comparison of I-ICA with basic ICA and 22 state-of-the-art algorithms shows I-ICA's superiority in terms of the rate of convergence and quality of the obtained solution.

关键词： Imperialist competitive algorithm constrained optimisation problems DE/rand/1 operator binomial crossover

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A modified group search optimiser for constrained optimisation problems

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INTERNATIONAL JOURNAL OF MODELLING IDENTIFICATION AND CONTROL 2013年第3期18卷 276-283页

作者： Wang, Lijin Zhong, Yiwen Fujian Agr & Forestry Univ Coll Comp & Informat Sci Fuzhou 350002 Peoples R China

The purpose of this study is to propose a modified group search optimiser (GSO) algorithm for constrained optimisation problems. Three strategies are designed to improve the performance of GSO algorithm. Levy flight strategy is employed for the producer to improve its exploitation ability, and also to simplify the computation. To further improve its exploitation ability, dimension-by-dimension search strategy is used to tackle the dimensions interference problem when producer uses levy flight strategy to find new position. Mutation strategy with adaptive controlling probability is used to improve the exploration ability by keeping the diversity of population. Using the superiority of feasible point scheme and the parameter free penalty scheme to handle constraints, the modified GSO algorithm was tested on 13 well-known benchmark problems. Comparisons are made with the standard GSO algorithm and other algorithms which use penalty function and various constraint handling methods. Results show the modified GSO algorithm is effective and competitive for constrained optimisation problems.

关键词： group search optimiser GSO levy flight dimension-by-dimension search mutation constrained optimisation problems

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High reliable disjoint path set selection in mobile ad-hoc network using Hopfield neural network

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IET COMMUNICATIONS 2011年第11期5卷 1566-1576页

作者： Sheikhan, M. Hemmati, E. Islamic Azad Univ Dept Elect Engn S Tehran Branch Tehran Iran

Mobile ad-hoc network (MANET) is a collection of wireless mobile computers forming a temporary network without any fixed infrastructure or wired backbone. Topological changes in MANET frequently render routing paths unusable. A suitable technique for addressing this problem is to enhance the diversity of paths between the source and destination. However, multipath routing is a challenging task. In particular, the correlation between the failures of the paths in a path set should be as small as possible. Shared nodes and links between the paths are usual failure points. Disjointed path sets require the multiple paths to be link-disjoint or node-disjoint. However, selecting an optimal path set is an NP-complete problem. Artificial neural networks have been proposed as computational tools to solve constrained optimisation problems. The use of Hopfield neural network as a path set selection algorithm is explored. Since this algorithm produces a set of backup paths with much higher reliability, it is beneficial for MANETs. We use link expiration time (LET) between two nodes to estimate link reliability. In this approach, node-disjoint and link-disjoint path sets can be found simultaneously with route discovery algorithm. So, if someone wants to find both node-disjoint and link-disjoint path sets, there is no need to submit extra control messages, as overhead, to the MANET. Simulation results show that the proposed protocol can find path sets with higher reliability in comparison to the other recent proposed algorithms.

关键词： wireless mobile computers wired backbone Communication network design, planning and routing Reliability mobile ad-hoc network Hopfield neural network mobile ad hoc networks telecommunication network routing node-disjoint path set Communications computing Neural computing techniques artificial neural networks control messages LET Mobile radio systems link expiration time link reliability estimation computational tools multipath routing link-disjoint path set route discovery algorithm telecommunication computing neural nets NP-complete problem constrained optimisation problems high reliable disjoint path set selection MANET telecommunication network reliability

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2D optimal MMF design with a controllable maximum loss for Doppler signals

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IET RADAR SONAR AND NAVIGATION 2018年第6期12卷 585-592页

作者： Wang, Yan He, Yuliang Sun, Sibo Fu, Jin Harbin Engn Univ Acoust Sci & Technol Lab Harbin 150001 Heilongjiang Peoples R China Harbin Engn Univ Coll Underwater Acoust Engn Harbin 150001 Heilongjiang Peoples R China Qingdao Haina Underwater Info Tech Co Ltd Qingdao 266400 Peoples R China

Mismatched filter (MMF) is a commonly used technique to suppress the masking effect of weak targets due to the range sidelobes of stronger targets. Although the existing design of MMFs is proved to have a good performance for radar, they cannot be directly applied to sonar, since the Doppler effect for sonar is far more severe than that for radar. In this study, the authors propose a novel two-dimensional (2D) optimal design of MMFs with a controllable maximum loss for sonar. First, they investigate the design of MMFs based on the 2D cross-correlation function when considering the Doppler effect. It is capable of improving the suppression performance of range sidelobes caused by Doppler signals. MMFs are extended to two dimensions. Then, the optimisation problems to minimise the integrated sidelobe level or the peak sidelobe level are established. It has the capability to flexibly control the maximum loss of signal-to-noise ratio and the 2D MMF characteristics by weighted functions. Finally, the constrained optimisation problems can be solved by transforming into the second-order cone programming problems. Simulations relative to multiple signals (P4 polyphase code, binary m-sequence and chirp signal) prove the validity of the design approach.

关键词： filtering theory radar signal processing Doppler effect correlation methods mathematical programming minimisation 2D optimal MMF design controllable maximum loss Doppler signals mismatched filter masking effect suppression range sidelobe suppression performance radar Doppler effect sonar two-dimensional optimal design 2D cross-correlation function integrated sidelobe level minimization peak sidelobe level signal-to-noise ratio 2D MMF characteristics weighted functions constrained optimisation problems second-order cone programming problems

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Performing linear convergence for distributed constrained optimisation over time-varying directed unbalanced networks

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IET CONTROL THEORY AND APPLICATIONS 2019年第17期13卷 2800-2810页

作者： Lu, Qingguo Li, Huaqing Wang, Zheng Han, Qi Ge, Wei Southwest Univ Coll Elect & Informat Engn Chongqing Key Lab Nonlinear Circuits & Intelligen Chongqing 400715 Peoples R China Chongqing Univ Sci & Technol Coll Elect & Informat Engn Chongqing 401331 Peoples R China Nanjing Univ Coll Chem & Chem Engn Nanjing 210046 Jiangsu Peoples R China

The problem of distributed constrained optimisation over a network of agents, where the goal is to cooperatively minimise the sum of all local convex objective functions is studied. Each agent in the network possesses only its private local convex objective function and is constrained to a coupling equality constraint and its local inequality constraint. Moreover, the authors particularly focus on the scenario where each agent is only allowed to interact with their in-neighbours over a series of time-varying directed unbalanced networks. To collectively address the optimisation problem, a novel distributed primal-dual push-DIGing (integrated push-sum strategy with distributed inexact gradient tracking method) algorithm (termed as DPD-PD) in which agents employ uncoordinated step-sizes is proposed. Unlike other methods, DPD-PD allows not only the mixing matrices are column-stochastic, but also the step-sizes are uncoordinated. An important feature of DPD-PD is handling distributed constrained optimisation problems in the case of time-varying directed unbalanced networks. When objective functions are strongly convex and smooth, the authors demonstrate that DPD-PD converges linearly to the optimal solution given that the uncoordinated step-sizes are smaller than an upper bound. Explicit convergence rate is also conducted. Preliminary results on some numerical experiments validate the theoretical findings.

关键词： stochastic processes matrix algebra convergence of numerical methods convex programming optimisation convergence gradient methods minimisation iterative methods integrated push-sum strategy distributed inexact gradient tracking method uncoordinated step-sizes constrained optimisation problems time-varying unbalanced networks DPD-PD converges performing linear convergence distributed constrained optimisation local convex objective functions private local convex objective function coupling equality constraint local inequality constraint optimisation problem primal-dual push-DIGing

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constrained optimisation and robust function optimisation with EIWO

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INTERNATIONAL JOURNAL OF BIO-INSPIRED COMPUTATION 2013年第2期5卷 84-98页

作者： Ramezani, Pezhman Ahangaran, Milad Yang, Xin-She KN Toosi Univ Technol Dept Ind Engn Tehran 19697 Iran Iran Univ Sci & Technol Dept Civil Engn Tehran *** Iran Middlesex Univ Sch Sci & Technol London NW4 4BT England Reykjavik Univ Sch Engn IS-101 Reykjavik Iceland

A robust variant of invasive weed optimisation (IWO) algorithm, called enhanced invasive weed optimisation (EIWO) algorithm, is proposed in this paper for the optimisation of constrained benchmark problems. Enjoying the ecological behaviour of colonising weeds, IWO has demonstrated its ability in solving different optimisation problems. Since making a proper balance between these two components is essential, especially to cope with constraint optimisation problems, two new rules are added to the algorithm to improve its performance. The first rule is utilising principles of social standard deviation as proposed in social harmony search (SHS) algorithm. The second rule is utilised to prevent the algorithm to get stuck on local optima. Finally, for constraint handling, three simple heuristic rules of Deb are utilised. The robustness and effectiveness of the proposed method are tested on many constrained benchmark problems and compared against those of state-of-the-art algorithms.

关键词： meta-heuristic algorithms enhanced invasive weed optimisation EIWO social harmony search algorithm constrained optimisation problems social spatial dispersion randomisation

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A systems-theoretic approach towards designing biological networks for perfect adaptation

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IFAC-PapersOnLine 2018年第1期51卷 307-312页

作者： Bhattacharya, Priyan Raman, Karthik Tangirala, Arun K. Madras Chennai600 036 India Madras Chennai600 036 India Madras Chennai600 036 India Madras Chennai600 036 India

Designing biological networks that are capable of achieving specific functionality has been of sustained interest in the field of synthetic biology for nearly a decade. Adaptation is one such important functionality that is observed in bacterial chemotaxis, cell signalling and homoeostasis. It refers to the ability of a cell to cope with environmental perturbations. All of these adaptation networks, involve negative feedback loops or open loop control strategies. A typical enzymatic network is a circuit of enzymes whose connections are characterized by enzymatic reactions that exhibit non-linear dynamics. Previous approaches to design of enzymatic networks capable of perfect adaptation have used brute force searches encompassing the complete set of possibilities to identify suitable circuit designs. In contrast, this work presents a systematic algorithm for circuit design, using a linear systems-theoretic approach. The key idea is to set up a design-oriented problem formulation as against employing a brute force search in the space of possible circuits. To this effect, we first linearize the non-linear dynamical circuit, subsequently, we translate the requirements for adaptation to design specifications for a linear time-invariant system and imposing these design specifications on the linearized system, we obtain the minimal topologies or motifs that can perform perfect adaptation, with an optimal set of rate constants. The optimal set of rate constants is obtained by solving a structure-specific constrained optimisation problem. In effect, we demonstrate that the proposed approach identifies the key motifs of the biological network that were identified by the existing brute force approach, albeit in a systematic manner and with very little computational effort. © 2018

关键词： Feedback Biochemistry Cell signaling constrained optimization Control systems Integrated circuit manufacture Invariance Linear systems Linearization Networks (circuits) Rate constants Robustness (control systems) Specifications Structural optimization Time varying control systems Adaptation constrained optimisation problems Environmental perturbations Linear time invariant systems Network motif Sensitivity Systems biology Systems theoretic approach

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Randomised block-coordinate Frank-Wolfe algorithm for distributed online learning over networks

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COGNITIVE COMPUTATION AND SYSTEMS 2020年第2期2卷 72-79页

作者： Li, Jingchao Wu, Qingtao Zheng, Ruijuan Zhu, Junlong Ge, Quanbo Zhang, Mingchuan Henan Univ Sci & Technol Sch Informat Engn Luoyang 471023 Peoples R China Tongji Univ Sch Elect & Informat Engn Shanghai 200092 Peoples R China

The distributed online algorithms which are based on the Frank-Wolfe method can effectively deal with constrained optimisation problems. However, the calculation of the full (sub)gradient vector in those algorithms leads to a huge computational cost at each iteration. To reduce the computational cost of the algorithms mentioned above, the authors present a distributed online randomised block-coordinate Frank-Wolfe algorithm over networks. Each agent in the networks only needs to calculate a subset of the coordinates of its (sub)gradient vector in this algorithm. Furthermore, they make a detailed theoretical analysis of the regret bound of this algorithm. When all local objective functions satisfy the conditions of strongly convex functions, the authors' algorithm attains the regret bound of O(root T), where T is the total number of iterations. Furthermore, the theorem results are verified via simulation experiments, which show that the algorithm is effective and efficient.

关键词： convex programming learning (artificial intelligence) gradient methods randomised algorithms computational complexity distributed online algorithms Frank-Wolfe method constrained optimisation problems distributed online learning randomised block-coordinate Frank-Wolfe algorithm full gradient vector subgradient vector convex functions

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Fog-cloud task scheduling of energy consumption optimisation with deadline consideration

Fog-cloud task scheduling of energy consumption optimisation...

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International Journal of Internet Manufacturing and Services

作者： Xu, Jiuyun Sun, Xiaoting Zhang, Ruru Liang, Hongliang Duan, Qiang School of Computer and Communication Engineering China University of Petroleum Qingdao China Software Technology Company No. 58 Kunshan Road Science and Technology City Suzhou High-Tech Zone Jiangsu Province China Department of Informatics Beijing University of Posts and Telecommunications Beijing China Information Sciences and Technology Department Pennsylvania State University PennsylvaniaPA United States

The emerging IoT introduces many new challenges that cannot be adequately addressed by the current 'cloud-only' architectures. The cooperation of the fog and cloud is considered to be a promising architecture, which efficiently handles IoT's data processing and communications requirements. However, how to schedule tasks to better adapt to IoT real-time needs and reduce the energy in the fog-cloud system is not well addressed. In this paper, we first model the energy consumption of the fog and cloud, respectively, and formulate a task scheduling problem into a constrained optimisation problem in fog-cloud computing system. Then, an efficient deadline-energy scheduling algorithm based on ant colony optimisation (DEACO) is put forward to tackle this problem, which achieves to reduce energy consumption on the condition of satisfying the task deadline. Finally, algorithms have been simulated on the extended CloudSim simulator. The experimental results have shown that our scheduling approach reduces energy more effective. © 2020 Inderscience Enterprises Ltd.

关键词： Green computing Ant colony optimization Computer architecture constrained optimization Data handling Energy utilization Fog Internet of things Multitasking Cloud systems Cloudsim constrained optimisation problems optimisations Real time Reduce energy consumption Task scheduling problem Task scheduling

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