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Review of nature and biologically inspired metaheuristics for greenhouse environment control

TRANSACTIONS OF THE INSTITUTE OF MEASUREMENT AND CONTROL 2020年第12期42卷 2338-2358页

作者： Oliveira, Paulo Moura Pires, E. J. Solteiro Boaventura-Cunha, Jose Pinho, Tatiana Martins Univ Tras Os Montes & Alto Douro UTAD Sch Sci & Technol P-5001801 Vila Real Portugal INESC TEC INESC Technol & Sci Porto Portugal

A significant number of search and optimisation techniques whose principles seek inspiration from nature and biology phenomena have been proposed in the last decades. These methods have been successfully applied to solve a wide range of engineering problems. This is also the case of greenhouse environment control, which has been incorporating this type of techniques into its design. This paper addresses evolutionary and bio-inspired methods in the context of greenhouse environment control. Algorithm principles for reference techniques are reviewed, namely: simulated annealing, genetic algorithm, differential evolution and particle swarm optimisation. The last three techniques are considered using single and multiple objective formulations. A review of these algorithms within greenhouse environment control applications is presented, considering single and multiple objective problems, as well as their current trends.

关键词： Greenhouse environment control evolutionary algorithm swarm optimisation differential evolution simulated annealing multi-objective optimisation bio-inspired algorithms

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bio-inspired auto-adaptive SIP overload controller

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TELECOMMUNICATION SYSTEMS 2014年第4期56卷 481-492页

作者： Chentouf, Zohair King Saud Univ Coll Informat & Comp Sci Dept Comp Sci Riyadh Saudi Arabia

In this article, a Session Initiation Protocol (SIP) overload control solution is proposed. It considers all the types of SIP requests. This is really what a SIP load is composed of, in an industrial environment. So far, the specialized literature considered INVITE messages only. So, we think that SIP servers are required to be dynamically adaptive to the diversity of the incoming load content. In the latter, the rate of a given SIP message type may be more or less than the other message types, depending on the services provided by the SIP server. Sometimes, it also depends on the time of the day. The auto-adaptation ability of the proposed overload control mechanism is designed after the immune system metaphor. The solution is validated through load tests and compared with a well known SIP overload control algorithm. Test load arrival patterns have been chosen to simulate three different service packages known in the SIP industry world as: Hosted Private Branch Exchange, Prepaid Calling Card Service, and Call-Shop Service.

关键词： SIP Overload control bio-inspired algorithms Artificial immune systems Auto-adaptive network applications

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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 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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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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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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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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A novel meta-heuristic search algorithm for solving optimization problems: capuchin search algorithm

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NEURAL COMPUTING & APPLICATIONS 2021年第7期33卷 2515-2547页

作者： Braik, Malik Sheta, Alaa Al-Hiary, Heba Al Balqa Appl Univ Salt Jordan Southern Connecticut State Univ Dept Comp Sci 501 Crescent St New Haven CT 06515 USA

Meta-heuristic search algorithms were successfully used to solve a variety of problems in engineering, science, business, and finance. Meta-heuristic algorithms share common features since they are population-based approaches that use a set of tuning parameters to evolve new solutions based on the natural behavior of creatures. In this paper, we present a novel nature-inspired search optimization algorithm called the capuchin search algorithm (CapSA) for solving constrained and global optimization problems. The key inspiration of CapSA is the dynamic behavior of capuchin monkeys. The basic optimization characteristics of this new algorithm are designed by modeling the social actions of capuchins during wandering and foraging over trees and riverbanks in forests while searching for food sources. Some of the common behaviors of capuchins during foraging that are implemented in this algorithm are leaping, swinging, and climbing. Jumping is an effective mechanism used by capuchins to jump from tree to tree. The other foraging mechanisms exercised by capuchins, known as swinging and climbing, allow the capuchins to move small distances over trees, tree branches, and the extremities of the tree branches. These locomotion mechanisms eventually lead to feasible solutions of global optimization problems. The proposed algorithm is benchmarked on 23 well-known benchmark functions, as well as solving several challenging and computationally costly engineering problems. A broad comparative study is conducted to demonstrate the efficacy of CapSA over several prominent meta-heuristic algorithms in terms of optimization precision and statistical test analysis. Overall results show that CapSA renders more precise solutions with a high convergence rate compared to competitive meta-heuristic methods.

关键词： Optimization Meta-heuristics bio-inspired algorithms Capuchin search algorithm Optimization techniques

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PAnDA: A Reconfigurable Architecture that Adapts to Physical Substrate Variations

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IEEE TRANSACTIONS ON COMPUTERS 2013年第8期62卷 1584-1596页

作者： Walker, James Alfred Trefzer, Martin A. Bale, Simon J. Tyrrell, Andy M. Univ York Dept Elect Intelligent Syst Grp York YO10 5DD N Yorkshire England

Field programmable gate arrays (FPGAs) are widely used in applications where online reconfigurable signal processing is required. Speed and function density of FPGAs are increasing as transistor sizes shrink to the nanoscale. As these transistors reduce in size intrinsic variability becomes more of a problem and to reliably create electronic designs according to specification time consuming statistical simulations become necessary;and even with accurate models and statistical simulation, the fabrication yield will decrease as every physical instance of a design behaves differently. This paper describes an adaptive, evolvable architecture that allows for correction and optimization of circuits directly in hardware using bioinspired techniques. Similar to FPGAs, the programmable analog and digital array (PAnDA) architecture introduced provides a digital configuration layer for circuit design. Accessing additional configuration options of the underlying analog layer enables continuous adjustment of circuit characteristics at runtime, which enables dynamic optimization of the mapped design's performance. Moreover, the yield of devices can be improved postfabrication via reconfiguration of the analog layer, which can overcome faults induced due to variability and process defects. Since optimization goals are generic, i.e., not restricted to reducing stochastic variability, power consumption or increasing speed, the same mechanisms can also enhance the device's fault tolerant abilities in the case of component degradation and failures during its lifetime or when exposed to hazardous environments.

关键词： Reconfigurable architectures intrinsic variability bio-inspired algorithms fault tolerance evolvable hardware adaptive hardware

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Special Issue: bio-inspired Optimization Techniques for High Performance Computing Preface

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NEW GENERATION COMPUTING 2011年第2期29卷 125-128页

作者： Folino, Gianluigi Mastroianni, Carlo ICAR CNR I-87036 Arcavacata Di Rende CS Italy

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A viral system algorithm to optimize the car dispatching in elevator group control systems of tall buildings

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COMPUTERS & INDUSTRIAL ENGINEERING 2013年第1期64卷 403-411页

作者： Cortes, Pablo Onieva, Luis Munuzuri, Jesus Guadix, Jose Univ Seville Escuela Tecn Super Ingn Ingn Org Seville 41092 Spain

Nowadays is very common the presence of tall buildings in the business centres of the main cities of the world. Such buildings require the installation of numerous lifts that are coordinated and managed under a unique control system. Population working in the buildings follows a similar traffic pattern generating situations of traffic congestion. The problem arises when a passenger makes a hall call wishing to travel to another floor of the building. The dispatching of the most suitable car is the optimization problem we are tackling in this paper. We develop a viral system algorithm which is based on a bio-inspired virus infection analogy to deal with it. The viral system algorithm is compared to genetic algorithms, and tabu search approaches that have proven efficiency in the vertical transportation literature. The experiments undertaken in tall buildings from 10 to 24 floors, and several car configurations from 2 to 6 cars, provide valuable results and show how viral system outperforms such soft computing algorithms. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Elevator Lift Viral system bio-inspired algorithms Elevator group control system Vertical transportation

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