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Moth and Ant inspired Routing in Hierarchical Airborne Networks with Multi-Beam Antennas

IEEE TRANSACTIONS ON MOBILE COMPUTING 2019年第4期18卷 910-922页

作者： Hu, Lei Hu, Fei Kumar, Sunil Univ Alabama Dept Elect & Comp Engn Tuscaloosa AL 35487 USA San Diego State Univ Elect & Comp Engn Dept San Diego CA 92182 USA

A set of novel routing protocols is proposed for directional hierarchical airborne networks. These networks use a two-level architecture, where the higher-level network is sparse with directional long-distance links that support the high data-rate communication. The lower-level network consists of high-density nodes with short-distance and low data-rate links. We assume that the higher-level nodes are equipped with multi-beam antennas, whereas the lower-level nodes have omni-directional antennas. We use the bio-inspired algorithms (based on moth and ant behaviors) to design the routing schemes for both levels. Specifically, we use the male moth's light source pursuing pattern for routing the data from an event node to the highly mobile sink in lower-level network, and the ant's chemical trail maintenance principle to trace the trajectory of commander node to deliver the data from lower-level node to the commander node in the higher-level network, with minimum delay. In addition, we construct a weighted fence routing topology among higher-level nodes with multi-beam antennas, in order to achieve high throughput. Our simulation results demonstrate that significant performance improvement is achieved by the bio-inspired routing schemes, compared with conventional ad hoc routing schemes.

关键词： Multi-beam antennas hierarchical wireless network airborne network ad hoc routing bio-inspired algorithms directional routing

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Improved bees algorithm for the deployment of homogeneous and heterogeneous wireless sensor networks

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INTERNATIONAL JOURNAL OF SENSOR NETWORKS 2022年第4期38卷 254-262页

作者： Deghbouch, Hicham Debbat, Fatima Univ Mustapha Stambouli Mascara Dept Comp Sci Mascara Algeria

Nowadays, countless applications benefit from the services provided by wireless sensor networks (WSNs) in remote surveillance and data gathering. Coverage is considered an important quality of service (QoS) criterion for many of these applications. To ensure that the required QoS is achieved, the sensors must be placed in locations that optimise the area coverage and eliminate the coverage holes, especially when deploying sensors with heterogeneous sensing ranges. In this paper, a novel sensor deployment scheme based on the improved bees algorithm (IBA) for optimising the area coverage in both homogeneous and heterogeneous WSNs is proposed. The IBA includes a neighbourhood shrinking procedure that aims to improve the local search efficiency and a site abandonment procedure for preventing the algorithm from the entrapment in a local optimum. Experiments show that the IBA delivers high-quality solutions and optimises the area coverage effectively compared with four state-of-the-art methods in homogeneous and heterogeneous WSNs.

关键词： wireless sensor networks WSNs bio-inspired algorithms metaheuristics coverage redundancy area coverage simulation optimisation

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Power tracking techniques for efficient operation of photovoltaic array in solar applications - A review

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RENEWABLE & SUSTAINABLE ENERGY REVIEWS 2019年第Mar.期101卷 82-102页

作者： Ahmad, Riaz Murtaza, Ali F. Sher, Hadeed Ahmed Univ Cent Punjab Elect Engn Dept Fac Engn Lahore Pakistan Ghulam Ishaq Khan Inst Engn Sci & Technol Fac Elect Engn Topi Pakistan

This paper presents a comprehensive overview on various maximum power point tracking (MPPT) techniques, which have been recently designed, simulated and/or experimentally validated in the PV literature. The primary goal of each MPPT technique is to optimize the output of shaded/unshaded photovoltaic (PV) array under static and dynamic weather conditions. Though each MPPT technique has its own pros and cons, an optimized MPPT technique is characterized in many aspects like hardware and software simplicity, implementation, cost effectiveness, sensors required, popularity, accuracy and convergence speed. In this paper the rating of various MPPT methods has been done based on the benchmark P&O method. The rating criteria is separately calculated for the techniques that are capable to work in full-sun and partial shading conditions. A rule based table is set to evaluate the MPPT against the algorithm's complexity, hardware implementation, tracking speed, and steady state accuracy or detection of global maximum. Moreover, special consideration has been given to bio-inspired MPPT algorithms. The bio-inspired algorithms are compared side by side with their specific application in PV system. A tree diagram is also designed to see the emergence of partial shading algorithms over a period of time. The traits presented in this paper are novel and provide bottom-line for the researchers to select and implement an appropriate MPPT technique.

关键词： Photovoltaic system MPPT techniques Conventional methods Partial shading bio-inspired algorithms

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Dynamic social behavior algorithm for real-parameter optimization problems and optimization of hyper beamforming of linear antenna arrays

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2017年 64卷 401-414页

作者： Sankar, K. Prajindra Kiong, Tiong Sieh Paw, Johnny Koh Siaw Univ Tenaga Nas Coll Engn Elect & Commun Engn Dept Kajang Selangor Malaysia Univ Tenaga Nas Inst Sustainable Energy Kajang Selangor Malaysia

The ever evolving complexity of real-world problems had become an impetus for the development of many new and efficient optimization algorithms. Meta-heuristics based on evolutionary computation and swarm intelligence are successful examples of nature-inspired optimization techniques. in this work, a new Dynamic Social Behavior (DSB) algorithm is proposed to solve global optimization problems. The DSB algorithm is based on the simulation of cooperative behavior of animal groups. In the proposed algorithm, individuals emulate the interaction of individuals based on biological laws of cooperative colony. This algorithm partially adopts the foraging strategy of animal groups and utilizes recruitment signal as a means of information transfer among individuals. In order to illustrate the proficiency and robustness of the proposed algorithm, it is compared with other well-known evolutionary algorithms. The comparison examines several series of widely used benchmark functions and an engineering problem on hyper beamforming optimization. The results testifies the superior performance of DSB compared with other state-of-the-art meta-heuristics. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Meta-heuristics Optimization Swarm algorithms Global optimization bio-inspired algorithms Beamforming

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Self-Chord: A bio-inspired P2P Framework for Self-Organizing Distributed Systems

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IEEE-ACM TRANSACTIONS ON NETWORKING 2010年第5期18卷 1651-1664页

作者： Forestiero, Agostino Leonardi, Emilio Mastroianni, Carlo Meo, Michela ICAR CNR Inst High Performance Comp & Networking I-87036 Arcavacata Di Rende Italy Politecn Torino Dept Elect I-10129 Turin Italy

This paper presents "Self-Chord," a peer-to-peer (P2P) system that inherits the ability of Chord-like structured systems for the construction and maintenance of an overlay of peers, but features enhanced functionalities deriving from ant-inspired algorithms, such as autonomous behavior, self-organization, and capacity to adapt to a changing environment. As opposed to the structured P2P systems deployed so far, resource indexing and placement is uncorrelated with network structure and topology, and resource keys are organized and managed by self-organizing mobile agents through simple local operations driven by probabilistic choices. Self-Chord has three main features that are particularly advantageous in Grid and Cloud Computing: 1) it is possible to give a semantic meaning to keys, which enables the execution of range queries;2) the keys are fairly distributed over the peers, thus improving the balancing of storage responsibilities;3) maintenance load is also limited because it is not necessary to reassign keys when new peers or resources are added to the system-the mobile agents will spontaneously reorganize the keys. The efficiency and effectiveness of Self-Chord were assessed both with a simulation framework and with an analytical model inspired by fluid dynamics.

关键词： bio-inspired algorithms cloud computing grid computing multiagent systems peer-to-peer (P2P) self-organization

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A novel multi-objective Interactive Coral Reefs Optimization algorithm for the Unequal Area Facility Layout Problem

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SWARM AND EVOLUTIONARY COMPUTATION 2020年 55卷 100688-100688页

作者： Garcia-Hernandez, L. Salas-Morera, L. Carmona-Munoz, C. Abraham, A. Salcedo-Sanz, S. Univ Cordoba Area Project Engn Cordoba Spain Machine Intelligence Res Labs MIR Labs Sci Network Innovat & Res Excellence Auburn WA USA Univ Alcala Dept Signal Proc & Commun Madrid Spain

The Unequal Area Facility Layout Problem (UA-FLP) has been widely analyzed in the literature using several heuristics and meta-heuristics to optimize some qualitative criteria, taking into account different restrictions and constraints. Nevertheless, the subjective opinion of the designer (Decision Maker, DM) has never been considered along with the quantitative criteria and restrictions. This work proposes a novel approach for the UA-FLP based on an Interactive Coral Reefs Optimization (ICRO) algorithm, which combines the simultaneous consideration of both quantitative and qualitative (DM opinion) features. The algorithm implementation is explained in detail, including the way of jointly considering quantitative and qualitative aspects in the fitness function of the problem. The experimental part of the paper illustrates the effect of including qualitative aspects in UA-FLP problems, considering three different hard UA-FLP instances. Empirical results show that the proposed approach is able to incorporate the DM preferences in the obtained layouts, without affecting much to the quantitative part of the solutions.

关键词： UA-FLP Coral Reefs Optimization Interactive algorithms bio-inspired algorithms

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A new algorithm inspired in the behavior of the social-spider for constrained optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2014年第2期41卷 412-425页

作者： Cuevas, Erik Cienfuegos, Miguel Univ Guadalajara Dept Elect CUCEI Guadalajara 44430 Jalisco Mexico

During the past decade, solving constrained optimization problems with swarm algorithms has received considerable attention among researchers and practitioners. In this paper, a novel swarm algorithm called the Social Spider Optimization (SSO-C) is proposed for solving constrained optimization tasks. The SSO-C algorithm is based on the simulation of cooperative behavior of social-spiders. In the proposed algorithm, individuals emulate a group of spiders which interact to each other based on the biological laws of the cooperative colony. The algorithm considers two different search agents (spiders): males and females. Depending on gender, each individual is conducted by a set of different evolutionary operators which mimic different cooperative behaviors that are typically found in the colony. For constraint handling, the proposed algorithm incorporates the combination of two different paradigms in order to direct the search towards feasible regions of the search space. In particular, it has been added: (1) a penalty function which introduces a tendency term into the original objective function to penalize constraint violations in order to solve a constrained problem as an unconstrained one;(2) a feasibility criterion to bias the generation of new individuals toward feasible regions increasing also their probability of getting better solutions. In order to illustrate the proficiency and robustness of the proposed approach, it is compared to other well-known evolutionary methods. Simulation and comparisons based on several well-studied benchmarks functions and real-world engineering problems demonstrate the effectiveness, efficiency and stability of the proposed method. (C) 2013 Elsevier Ltd. All rights reserved.

关键词： Swarm algorithms Constrained optimization bio-inspired algorithms

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An optimisation algorithm based on the behaviour of locust swarms

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INTERNATIONAL JOURNAL OF bio-inspired COMPUTATION 2015年第6期7卷 402-407页

作者： Cuevas, Erik Gonzalez, Adrian Zaldivar, Daniel Perez-Cisneros, Marco Univ Guadalajara CUCEI Dept Elect Guadalajara 44430 Jalisco Mexico

In this paper, a novel swarm algorithm called locust search (LS) is proposed for solving optimisation tasks. The LS algorithm is based on the simulation of the behaviour presented in swarms of locusts. In the proposed algorithm, individuals emulate a group of locusts which interact to each other based on the biological laws of the cooperative swarm. Experimental results demonstrate a high performance of the proposed method for searching a global optimum in comparison with other well-known evolutionary methods.

关键词： swarm algorithms global optimisation bio-inspired algorithms

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A Novel Feature Learning-Based bio-inspired Neural Network for Real-Time Collision-Free Rescue of Multirobot Systems

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2024年第11期71卷 14420-14429页

作者： Li, Junfei Yang, Simon X. Univ Guelph Sch Engn Adv Robot & Intelligent Syst Lab Guelph ON N1G 2W1 Canada

Natural disasters and urban accidents drive the demand for rescue robots to provide safer, faster, and more efficient rescue trajectories. In this article, a feature learning-based bio-inspired neural network (FLBBINN) is proposed to quickly generate a heuristic rescue path in complex and dynamic environments, as traditional approaches usually cannot provide a satisfactory solution to real-time responses to sudden environmental changes. The neurodynamic model is incorporated into the feature learning method that can use environmental information to improve path planning strategies. Task assignment and collision-free rescue trajectory are generated through robot poses and the dynamic landscape of neural activity. A dual-channel scale filter, a neural activity channel, and a secondary distance fusion are employed to extract and filter feature neurons. After completion of the feature learning process, a neurodynamics-based feature matrix is established to quickly generate the new heuristic rescue paths with parameter-driven topological adaptability. The proposed FLBBINN aims to reduce the computational complexity of the neural network-based approach and enable the feature learning method to achieve real-time responses to environmental changes. Several simulations and experiments have been conducted to evaluate the performance of the proposed FLBBINN. The results show that the proposed FLBBINN would significantly improve the speed, efficiency, and optimality for rescue operations.

关键词： bio-inspired algorithms feature learning mobile robots neural networks search and rescue

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New inspirations in swarm intelligence: a survey

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INTERNATIONAL JOURNAL OF bio-inspired COMPUTATION 2011年第1期3卷 1-16页

作者： Parpinelli, R. S. Lopes, H. S. Fed Univ Technol Parana UTFPR Bioinformat Lab BR-80230901 Curitiba Parana Brazil Santa Catarina State Univ UDESC Appl Cognit Comp Grp BR-89223100 Joinville SC Brazil

The growing;complexity of real-world problems has motivated computer scientists to search for efficient problem-solving methods. Evolutionary computation and swarm intelligence meta-heuristics are outstanding examples that nature has been an unending source of inspiration. The behaviour of bees, bacteria, glow-worms, fireflies, slime moulds, cockroaches, mosquitoes and other organisms have inspired swarm intelligence researchers to devise new optimisation algorithms. This tutorial highlights the most recent nature-based inspirations as metaphors for swarm intelligence meta-heuristics. We describe the biological behaviours from which a number of computational algorithms were developed. Also, the most recent and important applications and the main features of such meta-heuristics are reported.

关键词： swarm intelligence new meta-heuristics bio-inspired algorithms problem solving methods biological behaviours social living beings

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