scholarly journals Multi-Agent Modeling in Schedule Problems

2019 ◽  
Vol 09 (4) ◽  
pp. 100-111
Author(s):  
T.V. Sivakova ◽  
V.A. Sudakov
Keyword(s):  
Distributed Computing ◽  
Optimization Problems ◽  
Computational Experiments ◽  
Multi Agent Systems ◽  
Computing Environment ◽  
Agent Modeling ◽  
Agent Systems ◽  
Multi Agent

The article explores the use of multi-agent technologies for solving optimization problems. It is shown how multi-agent systems allow working with restrictions in a distributed computing environment. The task of scheduling is formalized. Software was developed and computational experiments were carried out, which showed the effectiveness of the proposed approach.

scholarly journals Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

Energies ◽  
2018 ◽  
Vol 11 (8) ◽  
pp. 1928 ◽  
Author(s):  
Alfonso González-Briones ◽  
Fernando De La Prieta ◽  
Mohd Mohamad ◽  
Sigeru Omatu ◽  
Juan Corchado
Keyword(s):  
Optimization Problems ◽  
State Of The Art ◽  
Smart Cities ◽  
Energy Optimization ◽  
Sensor Data ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Research Perspectives ◽  
Wide Range ◽  
Multi Agent

This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision-making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi-agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy-efficient solutions for smart cities and intelligent buildings.

The Science of Social Emergence

2009 ◽  
pp. 1-16 ◽  
Author(s):  
R. Keith Sawyer
Keyword(s):  
Scientific Study ◽  
Multi Agent Systems ◽  
Agent Communication ◽  
Agent Modeling ◽  
Agent Systems ◽  
Agent Communication Languages ◽  
Interaction Processes ◽  
Multi Agent ◽  
Rigorous Study ◽  
Social Emergence

Sociology should be the foundational science of social emergence. But to date, sociologists have neglected emergence, and studies of emergence are more common within microeconomics. Moving forward, I argue that a science of social emergence requires two advances beyond current approaches—and that sociology is better positioned than economics to make these advances. First, consistent with existing critiques of microeconomics, I argue that we need a more sophisticated representation of individual agents. Second, I argue that multi-agent models need a more sophisticated representation of interaction processes. The agent communication languages currently used by multi-agent systems researchers are not appropriate for modeling human societies. I conclude by arguing that the scientific study of interaction and emergence will have to migrate out of microeconomics and become a part of sociology. Sociologists, for their part, should embrace multi-agent modeling to pursue a more rigorous study of these traditional sociological issues.

scholarly journals A New Multi-Agent Approach for Solving Optimization Problems with High-Dimensional: Case Study in Email Spam Detection

2020 ◽  
Author(s):  
Hekmat Mohmmadzadeh ◽  
Farhad Soleimanian Gharehchopogh
Keyword(s):  
Optimization Problems ◽  
Population Diversity ◽  
Metaheuristic Algorithms ◽  
High Dimensional ◽  
Flower Pollination Algorithm ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Whale Optimization ◽  
Multi Agent ◽  
The Common

There exist numerous high-dimensional problems in the real world which cannot be solved through the common traditional methods. The metaheuristic algorithms have been developed as successful techniques for solving a variety of complex and difficult optimization problems. Notwithstanding their advantages, these algorithms may turn out to have weak points such as lower population diversity and lower convergence rate when facing complex high-dimensional problems. An appropriate approach to solve such problems is to apply multi-agent systems along with the metaheuristic algorithms. The present paper proposes a new approach based on the multi-agent systems and the concept of agent, which is named Multi-Agent Metaheuristic (MAMH) method. In the proposed approach, several basic and powerful metaheuristic algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Firefly Algorithm (FA), Bat Algorithm (BA), Flower Pollination Algorithm (FPA), Gray Wolf Optimizer (GWO), Whale Optimization Algorithm (WOA), Crow Search Algorithm (CSA), Farmland Fertility Algorithm (FFA), are considered as separate agents each of which sought to achieve its own goals while competing and cooperating with others to achieve the common goals. In overall, the proposed method was tested on 32 complex benchmark functions, the results of which indicated effectiveness and powerfulness of the proposed method for solving the high-dimensional optimization problems. In addition, in this paper, the binary version of the proposed approach, called Binary MAMH (BMAMH), was executed on the spam email dataset. According to the results, the proposed method exhibited a higher precision in detection of the spam emails compared to other metaheuristic algorithms and methods.

scholarly journals On cooperation in multi-agent systems

1997 ◽  
Vol 12 (3) ◽  
pp. 309-314 ◽  
Author(s):  
J. E. DORAN ◽  
S. FRANKLIN ◽  
N. R. JENNINGS ◽  
T. J. NORMAN
Keyword(s):  
Distributed Computing ◽  
Expert Systems ◽  
Object Oriented ◽  
Multi Agent Systems ◽  
Agent Based ◽  
Agent Systems ◽  
Key Concepts ◽  
Multi Agent ◽  
The Subject ◽  
Object Oriented Systems

Cooperation is often presented as one of the key concepts which differentiates multi-agent systems from other related disciplines such as distributed computing, object-oriented systems, and expert systems. However, it is a concept whose precise usage in agent-based systems is at best unclear and at worst highly inconsistent. Given the centrality of the issue, and the different ideological viewpoints on the subject, this was a lively panel which dealt with the following main issues.

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