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Recent nature-inspired algorithms for medical image segmentation based on tsallis statistics

COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION 2020年 88卷 105256-105256页

作者： Wachs-Lopes, G. A. Santos, R. M. Saito, N. T. Rodrigues, P. S. Inacian Educ Fdn Dept Comp Sci Sao Bernardo SP Brazil Inacian Educ Fdn Dept Elect Engn Sao Bernardo SP Brazil

Recently, many algorithms have emerged inspired by the biological behavior of animal life to deal with complicated applications such as combinatorial optimization. One of the most critical discussions involving these algorithms is concerning their objective functions. Also, recently, many works have demonstrated the efficiency of Tsallis non-extensive statistics in several applications. However, this formalism has not yet been tested in most recent bio-inspired algorithms as an evaluation function. Thus, this paper presents a study of seven of the most promising bio-inspired algorithms recently proposed (a maximum one decade), from this entropy applied to the multi-thresholding segmentation of medical images. The results show the range of values of q, the so-called non-extensivity parameter of the Tsallis entropy, for which the algorithms tested here have their best performance. It is also demonstrated that the Firefly algorithm (FFA) is the one that obtained the best performance in terms of segmentation, and Grey Wolf Optimizer (GWO) presents the fastest convergence. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Tsallis entropy Non-Extensive theory bio-inspired algorithms Medical image segmentation Optimization

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An efficient approach for load balancing of VMs in cloud environment

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APPLIED NANOSCIENCE 2021年第2期13卷 1313-1326页

作者： Assudani, Purshottam J. Balakrishnan, P. Vellore Inst Technol Sch Comp Sci & Engn Vellore Tamil Nadu India Shri Ramdeobaba Coll Engn & Management Dept Informat Technol Nagpur Maharashtra India

Cloud computing provides a number of resources over the internet to the users based on their request. These resources need to be scheduled in an efficient manner so that not only the provider gets benefited out of it, but the user also can take its advantage to the full extent. Therefore, resource scheduling is a critical and demanding requirement in a cloud environment. In this paper, we are proposing a bio-inspired approach, in which we have modified the existing particle swarm optimization (PSO) Algorithm and have combined it with genetic algorithm (GA) which in turn has the features and advantages of both the approaches. The proposed inventive particle swarm optimization with genetic algorithm (IPSO-GA) not only schedules resources efficiently, but also effectively manage the resources. The proposed approach is compared with traditional approaches on CloudSim simulator, where the proposed algorithm outperforms the traditional algorithms in terms of makespan time, execution time and resource utilization. Our proposed approach IPSO-GA has given better results than the existing approaches.

关键词： Cloud computing Resource scheduling Load balancing Resource utilization Resource mapping bio-inspired algorithms

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Optimizing tasks generation for children in the early stages of literacy teaching: a study using bio-inspired metaheuristics

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SOFT COMPUTING 2018年第20期22卷 6811-6824页

作者： de Souza, Gilberto Nerino, Jr. de Deus, Daniel Felipe Tadaiesky, Vincent de Araujo, Igor Meireles Monteiro, Dionne Cavalcante de Santana, Adamo Lima Fed Univ Para R Augusto Correa 01 BR-66075110 Belem PA Brazil

Behavioral teaching procedures can be used to promote the individualized learning of reading skills for children, and computational processes can assist instructors in the generation of a set of tasks. However, the automatic generation of these tasks can be unfeasible due to the high-order search space for the possible combinations of tasks;this complexity increases when considering the possible constraints as well as adapting the tasks to the individual characteristics of each student. This paper presents a new method to automatically generate teaching matching-to-sample tasks, adapting the difficulty by using bio-inspired optimization metaheuristics. Genetic algorithms, ant colony optimization, and integer and categorical particle swarm optimization were evaluated to determine their stability and capacity to generate adapted tasks. A comparison of the results between the algorithms showed a better rate of convergence for the genetic algorithms, which were able to generate tasks at an adapted level of difficulty to students. These tasks were applied to a group of students at a Brazilian public school in the early stages of a literacy course indicating satisfactory effects in the individual learning process.

关键词： Matching-to-sample procedure Teaching reading Tasks generation Adaptive difficulty Optimization metaheuristics bio-inspired algorithms

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An effective refined artificial bee colony algorithm for numerical optimisation

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INFORMATION SCIENCES 2019年 504卷 221-275页

作者： Bajer, Drazen Zoric, Bruno JJ Strossmayer Univ Osijek Fac Elect Engn Comp Sci & Informat Technol Osijek Kneza Trpimira 2b Osijek 31000 Croatia

Various complex problems have recently encouraged research and development of different bio-inspired optimisation algorithms, a well-known instance being the artificial bee colony (ABC) algorithm, both due to its simplicity and performance. Building upon the basic algorithm enabled further gains in performance but brought alongside it some specific costs and problems. The improved variants available in the literature often introduce additional user-defined parameters and sometimes completely infringe the algorithm structure. Focusing the search process on exploitation has proven to be a good first step of improvement in most cases, but analysing the effects of this modification on a limited set of standard benchmark functions could lead to a skewed perspective. This paper proposes a novel algorithm based on ABC that keeps the original structure intact, introduces a new solution update equation and an extended scout bee phase focusing the search on more prominent solutions without introducing new control parameters. Based on the conducted experimental analysis, it is able to outperform various competitive algorithms on a large test bed of benchmark functions and several real-world problems. The effects of the particular proposed modifications are also analysed and attention is given to two variants of the standard algorithm. (C) 2019 Elsevier Inc. All rights reserved.

关键词： Artificial bee colony bio-inspired algorithms Numerical optimisation Population diversity

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Effective multi-objective optimization with the coral reefs optimization algorithm

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ENGINEERING OPTIMIZATION 2016年第6期48卷 966-984页

作者： Salcedo-Sanz, S. Pastor-Sanchez, A. Portilla-Figueras, J. A. Prieto, L. Univ Alcala Escuela Politecn Super Dept Signal Proc & Commun Madrid Spain Iberdrola Dept Energy Resource Madrid Spain

In this article a new algorithm for multi-objective optimization is presented, the Multi-Objective Coral Reefs Optimization (MO-CRO) algorithm. The algorithm is based on the simulation of processes in coral reefs, such as corals' reproduction and fight for space in the reef. The adaptation to multi-objective problems is a process based on domination or non-domination during the process of fight for space in the reef. The final MO-CRO is an easily-implemented and fast algorithm, simple and robust, since it is able to keep diversity in the population of corals (solutions) in a natural way. The experimental evaluation of this new approach for multi-objective optimization problems is carried out on different multi-objective benchmark problems, where the MO-CRO has shown excellent performance in cases with limited computational resources, and in a real-world problem of wind speed prediction, where the MO-CRO algorithm is used to find the best set of features to predict the wind speed, taking into account two objective functions related to the performance of the prediction and the computation time of the regressor.

关键词： coral reefs optimization algorithm multi-objective optimization bio-inspired algorithms wind speed prediction extreme learning machines

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Carbon-aware Enterprise Network through Redesign

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COMPUTER JOURNAL 2015年第2期58卷 234-245页

作者： Habib, Sami Marimuthu, Paulvanna N. Zaeri, Naser Kuwait Univ Dept Comp Engn Safat 13060 Kuwait Arab Open Univ Fac Comp Studies Safat 13033 Kuwait

We present a novel green communication framework incorporating the redesign of existing campus enterprise network (CEN) to offset the carbon emissions at the backbone based on the theory of data encoding and power spectral density (PSD) estimations. CEN is viewed as a collection of service nodes distributed in various buildings, wherein the nodes are flexible to be clustered for serving a specific application. The carbon-offset at the backbone is indispensable as it is often susceptible to heavy traffic flow, thereby necessitating high-power cooling equipment to reduce the intensive heat spots generated from the equipment, such as routers and servers. The proposed framework accounts carbon emission from the average power consumed by the transmitted data through integrating the area under PSD curve of the encoded-transmitted data. The redesign problem is formulated as an optimization problem to redistribute the heavily communicating nodes at the backbone and to dispense the heat spots away from the backbone. We have utilized two bio-inspired algorithms such as Genetic Algorithm (GA) and Simulated Annealing (SA) to search the redesign space. The simulation results with the Manchester encoder within GA offer a maximum reduction of 23.6% of annual carbon emissions when compared with the initial CEN, whereas the SA reduces the carbon emission by 16%.

关键词： carbon emission green communications redesign bio-inspired algorithms Manchester encoding polar encoding power spectral density

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A review of techniques for online control of parameters in swarm intelligence and evolutionary computation algorithms

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INTERNATIONAL JOURNAL OF bio-inspired COMPUTATION 2019年第1期13卷 1-20页

作者： Parpinelli, Rafael Stubs Plichoski, Guilherme Felippe da Silva, Renan Samuel Narloch, Pedro Henrique Santa Catarina State Univ Grad Program Appl Comp Joinville SC Brazil

The two major groups representing biologically inspired algorithms are swarm intelligence (SI) and evolutionary computation (EC). Both SI and EC share common features such as the use of stochastic components during the optimisation process and various parameters for configuration. The setup of parameters in swarm and in evolutionary algorithms has an important role in defining their behaviour, guiding the search and biasing the quality of final solutions. In addition, an appropriate setting for the parameters may change during the optimisation process making this task even harder. The present work brings an up-to-date discussion focusing on online parameter control strategies applied in SI and EC. Also, this review analyses and points out the key techniques and algorithms used and suggests some directions for future research.

关键词： parameter control bio-inspired algorithms meta-heuristics natural computing parameterless algorithms

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A survey of symbiotic organisms search algorithms and applications

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NEURAL COMPUTING & APPLICATIONS 2020年第2期32卷 547-566页

作者： Abdullahi, Mohammed Ngadi, Md Asri Dishing, Salihu Idi Abdulhamid, Shafi'i Muhammad Usman, Mohammed Joda Ahmadu Bello Univ Dept Comp Sci Zaria Nigeria Univ Teknol Malaysia Fac Comp Dept Comp Sci Johor Baharu 81310 Malaysia Fed Univ Technol Minna Dept Cyber Secur Sci Minna Nigeria Bauchi State Univ Gadau Dept Math PMB 068 Bauchi Bauchi State Nigeria

Nature-inspired algorithms take inspiration from living things and imitate their behaviours to accomplish robust systems in engineering and computer science discipline. Symbiotic organisms search (SOS) algorithm is a recent metaheuristic algorithm inspired by symbiotic interaction between organisms in an ecosystem. Organisms develop symbiotic relationships such as mutualism, commensalism, and parasitism for their survival in ecosystem. SOS was introduced to solve continuous benchmark and engineering problems. The SOS has been shown to be robust and has faster convergence speed when compared with genetic algorithm, particle swarm optimization, differential evolution, and artificial bee colony which are the traditional metaheuristic algorithms. The interests of researchers in using SOS for handling optimization problems are increasing day by day, due to its successful application in solving optimization problems in science and engineering fields. Therefore, this paper presents a comprehensive survey of SOS advances and its applications, and this will be of benefit to the researchers engaged in the study of SOS algorithm.

关键词： Symbiotic organisms search Metaheuristics algorithms Optimization bio-inspired algorithms Local search Global search

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A comprehensive survey of the Grasshopper optimization algorithm: results, variants, and applications

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NEURAL COMPUTING & APPLICATIONS 2020年第19期32卷 15533-15556页

作者： Abualigah, Laith Diabat, Ali Amman Arab Univ Fac Comp Sci & Informat Amman Jordan New York Univ Abu Dhabi Div Engn Abu Dhabi 129188 Saadiyat Island U Arab Emirates NYU Tandon Sch Engn Dept Civil & Urban Engn Brooklyn NY 11201 USA

The grasshopper optimization algorithm is one of the dominant modern meta-heuristic optimization algorithms. It has been successfully applied to various optimization problems in several fields, including engineering design, wireless networking, machine learning, image processing, control of power systems, and others. We survey the available literature on the grasshopper optimization algorithm, including its modifications, hybridizations, and generalization to the binary, chaotic, and multi-objective cases. We review its applications, evaluate the algorithms, and provide conclusions.

关键词： Grasshopper optimization algorithm Meta-heuristic optimization algorithms Optimization problems 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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