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Jan Stypka, Piotr Anielski, Szymon Mentel, Daniel Krzywicki, Wojciech Turek, Aleksander Byrski, Marek Kisiel-Dorohinicki
2016 Computer Science  
In this paper, we describe an easy and efficient way to implement certain population-based algorithms (in the discussed case, multi-agent computing system) on such runtime environments.  ...  Computing applications such as metaheuristics-based optimization can greatly benefit from multi-core architectures available on modern supercomputers.  ...  energy agent genotype energy Environment high energy: reproduction low energy: death evaluation and energy transfer A A immigration A emigration Environment Environment A  ... 
doi:10.7494/csci.2016.17.1.83 fatcat:4ts2mcmnz5hajafl33a7wcsq2u


Mohamed Yasin Noor Mohamed, Sultan Qaboos University, Muscat, Oman
2019 International Journal of Advanced Trends in Computer Science and Engineering  
This part of interest leads the researchers to produce many algorithms for solving MEB along with hybrid models for efficient local search procedure.  ...  Literature survey based on evolutionary algorithms provides, author, title of the paper, year of publication, issues described, constraints considered, mapping of MEB with the proposed method, algorithm  ...  Soumyadip Sengupta, et al [34] in the year 2012 proposed a paper on Energy-Efficient Differentiated Coverage of Dynamic Objects using an Improved Evolutionary Multi-objective optimization Algorithm with  ... 
doi:10.30534/ijatcse/2019/1481.22019 fatcat:4dwzt6evm5apfgtulbjvjj5p7q

A multi-agent based evolutionary algorithm in non-stationary environments

Yang Yan, Hongfeng Wang, Dingwei Wang, Shengxiang Yang, Dazhi Wang
2008 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence)  
In this paper, a multi-agent based evolutionary algorithm (MAEA) is introduced to solve dynamic optimization problems. The agents simulate living organism features and co-evolve to find optimum.  ...  All agents live in a lattice like environment, where each agent is fixed on a lattice point.  ...  [25] integrated multi-agent systems with GAs to form a new algorithm for solving the global numerical optimization problem.  ... 
doi:10.1109/cec.2008.4631198 dblp:conf/cec/YanWWYW08 fatcat:a7lqismgivfirbepidrvzjgsky

Agent Based Evolutionary Dynamic Optimization [chapter]

Yang Yan, Shengxiang Yang, Dazhi Wang, Dingwei Wang
2010 Agent-Based Evolutionary Search  
In the proposed algorithm, all agents live in a lattice like environment, where each agent is fixed on a lattice point.  ...  This chapter investigates an agent based evolutionary search algorithm in which the agents are updated and co-evolve to track dynamic optimum by imitating the exhibited feature of living organism.  ...  Another interesting work is to further investigate the idea of AES algorithm to solve other optimization problems with sequential and real encodings in non-stationary environments.  ... 
doi:10.1007/978-3-642-13425-8_5 fatcat:vcwzevytjvfjhg676pklrmpx24

A Survey on Recent Trends of PIO and Its Variants Applied for Motion Planning of Dynamic Agents [chapter]

Muhammad Shafiq, Zain Anwar Ali, Eman H. Alkhammash
2021 Motion Planning [Working Title]  
Pigeon Inspired Optimization (PIO) algorithm is gaining popularity since its development due to faster convergence ability with great efficiencies when compared with other bio-inspired algorithms.  ...  The navigation capability of homing pigeons has been precisely used in Pigeon Inspired Optimization algorithm and continuous advancement in existing algorithms is making it more suitable for complex optimization  ...  [51] "A novel adaptive pigeon-inspired optimization algorithm based on evolutionary game theory" adaptive pigeon-inspired optimization algorithm Evolutionary game theory (EGT) pigeons PIO CPIO  ... 
doi:10.5772/intechopen.99881 fatcat:rcltdyrpufgrxm55pbscdtd4wm

Evolutionary multi-agent systems

Aleksander Byrski, Rafał Dreżewski, Leszek Siwik, Marek Kisiel-Dorohinicki
2015 Knowledge engineering review (Print)  
The aim of this paper is to give a survey on the development and applications of evolutionary multi-agent systems (EMAS).  ...  This includes dynamically changing environmental conditions, lack of global knowledge, no generational synchronisation, co-evolution of species, evolving genotype-phenotype mapping, etc.  ...  N N516 500039 "Biologically inspired mechanisms in planning and management of dynamic environments" and AGH University of Science and Technology statutory fund.  ... 
doi:10.1017/s0269888914000289 fatcat:p55jq657hjhczp7sht2r7wavju

Computing agents for decision support systems

D. Krzywicki, Ł. Faber, A. Byrski, M. Kisiel-Dorohinicki
2014 Future generations computer systems  
We recall as an example the concept of Evolutionary Multi-Agent Systems, which combine evolutionary and agent computing paradigms.  ...  In this paper, we show how multi-agent systems can fulfil these requirements.  ...  MAS4EVO (Multi-Agent System for EVolutionary Optimization).  ... 
doi:10.1016/j.future.2014.02.002 fatcat:l26xxp5n4be7dlofkfdmze4mde

Table of Contents

2020 2020 IEEE Symposium Series on Computational Intelligence (SSCI)  
Optimization of the Spatial Positioning of Agents in Virtual Environments Marcos S.  ...  Evolutionary Algorithm based on R2 Indicator for Pickup and Delivery Problem with Time Windows Li Li, Avimanyu Sahoo and Liang Chang .......... 1315 Deception in A Multi-agent Adversarial Game: The Game  ... 
doi:10.1109/ssci47803.2020.9308155 fatcat:hyargfnk4vevpnooatlovxm4li

Parallel differential evolution approach for cloud workflow placements under simultaneous optimization of multiple objectives

Daniel Balouek-Thomert, Arya K. Bhattacharya, Eddy Caron, Karunakar Gadireddy, Laurent Lefevre
2016 2016 IEEE Congress on Evolutionary Computation (CEC)  
The head of a hierarchy is termed as Master Agent (MA) whereas the others are Local Agents (LA).  ...  Differential Evolution The developments in Multi-Objective Evolutionary Algorithms referred in Section I have been along the track of Genetic Algorithm (GA) [13] , the baseline Evolutionary optimization  ... 
doi:10.1109/cec.2016.7743876 dblp:conf/cec/Balouek-Thomert16 fatcat:2gs33grylraiho733lmri4l3k4

Symbiotic Communications: Where Marconi Meets Darwin [article]

Ying-Chang Liang, Ruizhe Long, Qianqian Zhang, Dusit Niyato
2021 arXiv   pre-print
In symbiotic coevolution, each SR is empowered with an evolutionary cycle alongside the multi-agent learning, while in symbiotic synthesis, the SRs ingeniously optimize their operating parameters and transmission  ...  protocols by solving a multi-objective optimization problem.  ...  In multi-agent reinforcement learning, each SR learns its own best policy individually through interacting with other SRs and its environment over time via trial and error [12] .  ... 
arXiv:2103.16142v1 fatcat:yrutldvyhjbhzfk5jymcuye5tu

Reducing Efficiency of Connectivity-Splitting Attack on Newscast via Limited Gossip [chapter]

Jakub Muszyński, Sébastien Varrette, Pascal Bouvry
2016 Lecture Notes in Computer Science  
Efficient Hill Climber for Multi-Objective Pseudo-Boolean Optimization Francisco Chicano, Darrell Whitley, Renato Tinós Local search algorithms and iterated local search algorithms are a basic technique  ...  energies, an optimized operation of energy systems is important.  ... 
doi:10.1007/978-3-319-31204-0_20 fatcat:27rnwllk75cv5kncys2u7utreq

A one decade survey of autonomous mobile robot systems

Noor Abdul Khaleq Zghair, Ahmed S. Al-Araji
2021 International Journal of Power Electronics and Drive Systems (IJPEDS)  
It must be observed and integrated so that the robot functions properly: Navigation systems, localization systems, detection systems (sensors) along with motion and kinematics and dynamics systems.  ...  . − Particle swarm optimization (PSO): Is another evolutionary algorithm widely used in path planning.  ...  Categories of path planning algorithms Over time, they were supplemented via optimization approaches attempting on reducing the distance that is traveled via mobile robots.  ... 
doi:10.11591/ijece.v11i6.pp4891-4906 fatcat:mqdf7l3vhbe2xia7jftst3txoa

Artificial Intelligence Aided Next-Generation Networks Relying on UAVs [article]

Xiao Liu, Mingzhe Chen, Yuanwei Liu, Yue Chen, Shuguang Cui, Lajos Hanzo
2020 arXiv   pre-print
Moreover, AI enables the interaction amongst a swarm of UAVs for cooperative optimization of the system.  ...  Artificial intelligence (AI) assisted unmanned aerial vehicle (UAV) aided next-generation networking is proposed for dynamic environments.  ...  Therefore, the multi-agent RL algorithm is chosen for tackling this problem.  ... 
arXiv:2001.11958v1 fatcat:i35weka7wndghp3folyzsd4mi4

Multi-grid cellular genetic algorithm for optimizing variable ordering of ROBDDs

Cristian Rotaru, Octav Brudaru
2012 2012 IEEE Congress on Evolutionary Computation  
This paper presents a cellular genetic algorithm for optimizing the variable order in Reduced Ordered Binary Decision Diagrams. The evolution process is inspired by a basic genetic algorithm.  ...  The extensive experimental evaluation uses difficult classical benchmarks and proves the efficiency and the stability of the algorithm.  ...  Vasilakos and Witold Pedrycz, Energy-Efficient Differentiated Coverage of Dynamic Objects using an Improved Evolutionary Multi-objective optimization Algorithm with Fuzzy-Dominance Wednesday, IEEE CEC,  ... 
doi:10.1109/cec.2012.6256590 dblp:conf/cec/RotaruB12 fatcat:4ly3nrktw5habc6lf5err7d5py

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications [article]

Khaled B. Letaief, Yuanming Shi, Jianmin Lu, Jianhua Lu
2021 arXiv   pre-print
New design principles of wireless networks, service-driven resource allocation optimization methods, as well as a holistic end-to-end system architecture to support edge AI will be described.  ...  However, state-of-the-art deep learning and big data analytics based AI systems require tremendous computation and communication resources, causing significant latency, energy consumption, network congestion  ...  Besides, the learned algorithms can be distributively executed in the multi-agent edge AI systems.  ... 
arXiv:2111.12444v1 fatcat:crrbtfylvjeihogumggdnxcbpq
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