scholarly journals Solvability of pseudobulous conditional optimization problems of the type of many salesmen

2021 ◽  
pp. 30-55
Author(s):  
M. S. Germanchuk
Keyword(s):  
Optimization Problems ◽  
Distance Matrix ◽  
Original Data ◽  
System Management ◽  
Common Solution ◽  
Multi Agent ◽  
Np Complete ◽  
Polynomially Solvable ◽  
Normal Shape ◽  
Conditional Optimization

Formalizing routing problems of many traveling salesman (mTSP) in complex networks leads to NP-complete pseudobulous conditional optimization problems. The subclasses of polynomially solvable problems are distinguished, for which the elements of the distance matrix satisfy the triangle inequality and other special representations of the original data. The polynomially solvable assignment problem can be used to determine the required number of salesmen and to construct their routes. Uses a subclass of tasks in the form of pseudobulous optimization with disjunctive normal shape (\textit{DNS}) constraints to which the task is reduced mTSP. Problems in this form are polynomially solvable and allow to combine knowledge about network structure, requirements to pass routes by agents (search procedures) and efficient algorithms of logical inference on constraints in the form of \textit{DNS}. This approach is the theoretical justification for the development of multi-agent system management leading to a solution mTSP. Within the framework of intellectual planning, using resources and capabilities, and taking into account the constraints for each agent on the selected clusters of the network, the construction of a common solution for the whole complex network is achieved.

scholarly journals Boltzmann Distributed Replicator Dynamics: Population Games in a Microgrid Context

Games ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 8
Author(s):  
Gustavo Chica-Pedraza ◽  
Eduardo Mojica-Nava ◽  
Ernesto Cadena-Muñoz
Keyword(s):  
Control Method ◽  
Optimization Problems ◽  
Replicator Dynamics ◽  
Boltzmann Distribution ◽  
Full Information ◽  
Multi Agent Systems ◽  
Q Learning ◽  
Population Games ◽  
Distributed Approach ◽  
Multi Agent

Multi-Agent Systems (MAS) have been used to solve several optimization problems in control systems. MAS allow understanding the interactions between agents and the complexity of the system, thus generating functional models that are closer to reality. However, these approaches assume that information between agents is always available, which means the employment of a full-information model. Some tendencies have been growing in importance to tackle scenarios where information constraints are relevant issues. In this sense, game theory approaches appear as a useful technique that use a strategy concept to analyze the interactions of the agents and achieve the maximization of agent outcomes. In this paper, we propose a distributed control method of learning that allows analyzing the effect of the exploration concept in MAS. The dynamics obtained use Q-learning from reinforcement learning as a way to include the concept of exploration into the classic exploration-less Replicator Dynamics equation. Then, the Boltzmann distribution is used to introduce the Boltzmann-Based Distributed Replicator Dynamics as a tool for controlling agents behaviors. This distributed approach can be used in several engineering applications, where communications constraints between agents are considered. The behavior of the proposed method is analyzed using a smart grid application for validation purposes. Results show that despite the lack of full information of the system, by controlling some parameters of the method, it has similar behavior to the traditional centralized approaches.

ONE IMPROVED AGENT GENETIC ALGORITHM — RING-LIKE AGENT GENETIC ALGORITHM FOR GLOBAL NUMERICAL OPTIMIZATION

2009 ◽  
Vol 26 (04) ◽  
pp. 479-502 ◽  
Author(s):  
BIN LIU ◽  
TEQI DUAN ◽  
YONGMING LI
Keyword(s):  
Genetic Algorithm ◽  
Numerical Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Genetic Operators ◽  
Global Numerical Optimization ◽  
Candidate Solution ◽  
Low Dimension ◽  
Multi Agent ◽  
Agent Structure

In this paper, a novel genetic algorithm — dynamic ring-like agent genetic algorithm (RAGA) is proposed for solving global numerical optimization problem. The RAGA combines the ring-like agent structure and dynamic neighboring genetic operators together to get better optimization capability. An agent in ring-like agent structure represents a candidate solution to the optimization problem. Any agent interacts with neighboring agents to evolve. With dynamic neighboring genetic operators, they compete and cooperate with their neighbors, and they can also use knowledge to increase energies. Global numerical optimization problems are the most important ones to verify the performance of evolutionary algorithm, especially of genetic algorithm and are mostly of interest to the corresponding researchers. In the corresponding experiments, several complex benchmark functions were used for optimization, several popular GAs were used for comparison. In order to better compare two agents GAs (MAGA: multi-agent genetic algorithm and RAGA), the several dimensional experiments (from low dimension to high dimension) were done. These experimental results show that RAGA not only is suitable for optimization problems, but also has more precise and more stable optimization results.

Multi-Agent system management of business processes as an intellectual approach to resource conservation

2021 ◽  
Author(s):  
E. V. Shirinkina ◽  
A. V. Sorochkin ◽  
A. V. Kuramshina ◽  
B. Sh. Sobirov ◽  
O. Ja. Kravets
Keyword(s):  
Business Processes ◽  
Resource Conservation ◽  
System Management ◽  
Multi Agent System ◽  
Agent System ◽  
Multi Agent

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.

scholarly journals Multi-Context System for Optimization Problems

2019 ◽  
Vol 33 ◽  
pp. 2929-2937
Author(s):  
Tiep Le ◽  
Tran Cao Son ◽  
Enrico Pontelli
Keyword(s):  
Complexity Analysis ◽  
Optimization Problems ◽  
Distributed Optimization ◽  
Multi Agent System ◽  
Constraint Optimization ◽  
Agent System ◽  
Distributed Constraint Optimization ◽  
Multi Agent ◽  
Definition Of ◽  
Constraint Optimization Problems

This paper proposes Multi-context System for Optimization Problems (MCS-OP) by introducing conditional costassignment bridge rules to Multi-context Systems (MCS). This novel feature facilitates the definition of a preorder among equilibria, based on the total incurred cost of applied bridge rules. As an application of MCS-OP, the paper describes how MCS-OP can be used in modeling Distributed Constraint Optimization Problems (DCOP), a prominent class of distributed optimization problems that is frequently employed in multi-agent system (MAS) research. The paper shows, by means of an example, that MCS-OP is more expressive than DCOP, and hence, could potentially be useful in modeling distributed optimization problems which cannot be easily dealt with using DCOPs. It also contains a complexity analysis of MCS-OP.

A Hybrid Approach of Heuristic and Neural Network for Packing Problems

1994 ◽  
Author(s):  
Zuo Dai ◽  
Jianzhong Cha
Keyword(s):  
Neural Network ◽  
Large Scale ◽  
Optimization Problems ◽  
Travelling Salesman Problem ◽  
Hybrid Approach ◽  
Two Dimensions ◽  
Packing Problems ◽  
Combinatorial Optimization Problems ◽  
Complete Problems ◽  
Np Complete

Abstract Artificial Neural Networks, particularly the Hopfield-Tank network, have been effectively applied to the solution of a variety of tasks formulated as large scale combinatorial optimization problems, such as Travelling Salesman Problem and N Queens Problem [1]. The problem of optimally packing a set of geometries into a space with finite dimensions arises frequently in many applications and is far difficult than general NP-complete problems listed in [2]. Until now within accepted time limit, it can only be solved with heuristic methods for very simple cases (e.g. 2D layout). In this paper we propose a heuristic-based Hopfield neural network designed to solve the rectangular packing problems in two dimensions, which is still NP-complete [3]. By comparing the adequacy and efficiency of the results with that obtained by several other exact and heuristic approaches, it has been concluded that the proposed method has great potential in solving 2D packing problems.

Neural Networks Model for Optimization an Study on Isomorphism Discernment

2010 ◽  
Vol 156-157 ◽  
pp. 492-495
Author(s):  
Miao Zhang ◽  
Ning Bo Liao ◽  
Chen Zhou
Keyword(s):  
Neural Networks ◽  
Large Scale ◽  
Optimization Problems ◽  
Isomorphism Problem ◽  
Processing Elements ◽  
Feedback Network ◽  
Simple Processing ◽  
Np Complete ◽  
Is Design ◽  
Complete Optimization

An artificial neural network is composed of large number of simple processing elements by direct links named connections, the benefits of neural networks extend beyond the high computation rates by massive parallelism. Optimization problems could be transferred into a feedback network, the network interconnects the neurons with a feedback path. Graphs isomorphism discernment is one of the most important and difficult issues in graphs theory based structures design. To solve the problem, a Hopfield neural networks (HNN) model is presented in this paper. The solution of HNN is design as a permutation matrix of two graphs, and some operators are improved to prevent premature convergence. It is concluded that the algorithm presented here is efficient for large-scale graphs isomorphism problem and other NP-complete optimization issues.

Sign in / Sign up

Export Citation Format

Share Document