A New Hybrid Distributed Double Guided Genetic Swarm Algorithm for Optimization and Constraint Reasoning

2012 ◽  
Vol 3 (2) ◽  
pp. 63-74 ◽  
Author(s):  
Asma Khadhraoui ◽  
Sadok Bouamama
Keyword(s):  
Particle Swarm Optimization ◽  
Constraint Satisfaction ◽  
Hybrid Algorithm ◽  
Optimal Solution ◽  
Constraint Satisfaction Problems ◽  
Multi Agent Systems ◽  
Swarm Optimization ◽  
Agent Systems ◽  
Constraint Reasoning ◽  
Multi Agent

In this paper the authors propose a new distributed double guided hybrid algorithm combining the particle swarm optimization (PSO) with genetic algorithms (GA) to resolve maximal constraint satisfaction problems (Max-CSPs). It consists on a multi-agent approach inspired by a centralized version of hybrid algorithm called Genetical Swarm Optimization (GSO). Their approach consists of a set of evolutionary agents dynamically created and cooperating in order to find an optimal solution. Each agent executes its own hybrid algorithm and it is able to compute its own parameters. The authors’ approach is compared to the GSO. It demonstrates its superiority. They reached these results thanks to the distribution using multi-agent systems, diversification and intensification mechanisms.

scholarly journals Asynchronous Partial Overlay: A New Algorithm for Solving Distributed Constraint Satisfaction Problems

2006 ◽  
Vol 25 ◽  
pp. 529-576 ◽  
Author(s):  
R. Mailler ◽  
V. R. Lesser
Keyword(s):  
Constraint Satisfaction ◽  
Constraint Satisfaction Problems ◽  
Mediation Process ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Distributed Constraint Satisfaction ◽  
Systems Research ◽  
Multi Agent ◽  
Critical Paths ◽  
Real World Problems

Distributed Constraint Satisfaction (DCSP) has long been considered an important problem in multi-agent systems research. This is because many real-world problems can be represented as constraint satisfaction and these problems often present themselves in a distributed form. In this article, we present a new complete, distributed algorithm called Asynchronous Partial Overlay (APO) for solving DCSPs that is based on a cooperative mediation process. The primary ideas behind this algorithm are that agents, when acting as a mediator, centralize small, relevant portions of the DCSP, that these centralized subproblems overlap, and that agents increase the size of their subproblems along critical paths within the DCSP as the problem solving unfolds. We present empirical evidence that shows that APO outperforms other known, complete DCSP techniques.

scholarly journals Using Particle Swarm Optimization for Source Seeking in Multi-Agent Systems

2017 ◽  
Vol 50 (1) ◽  
pp. 11427-11433 ◽  
Author(s):  
Marcus Gronemeyer ◽  
Marcus Bartels ◽  
Herbert Werner ◽  
Joachim Horn
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Multi Agent Systems ◽  
Swarm Optimization ◽  
Agent Systems ◽  
Source Seeking ◽  
Multi Agent

scholarly journals A projection-based continuous-time algorithm for distributed optimization over multi-agent systems

2021 ◽  
Author(s):  
Xingnan Wen ◽  
Sitian Qin
Keyword(s):  
Continuous Time ◽  
Optimization Problem ◽  
Distributed Optimization ◽  
Optimal Solution ◽  
Fixed Time ◽  
Time Algorithm ◽  
State Variables ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent

AbstractMulti-agent systems are widely studied due to its ability of solving complex tasks in many fields, especially in deep reinforcement learning. Recently, distributed optimization problem over multi-agent systems has drawn much attention because of its extensive applications. This paper presents a projection-based continuous-time algorithm for solving convex distributed optimization problem with equality and inequality constraints over multi-agent systems. The distinguishing feature of such problem lies in the fact that each agent with private local cost function and constraints can only communicate with its neighbors. All agents aim to cooperatively optimize a sum of local cost functions. By the aid of penalty method, the states of the proposed algorithm will enter equality constraint set in fixed time and ultimately converge to an optimal solution to the objective problem. In contrast to some existed approaches, the continuous-time algorithm has fewer state variables and the testification of the consensus is also involved in the proof of convergence. Ultimately, two simulations are given to show the viability of the algorithm.

scholarly journals A Hybrid Algorithm of Particle Swarm Optimization and Tabu Search for Distribution Network Reconfiguration

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Sidun Fang ◽  
Xiaochen Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Tabu Search ◽  
Hybrid Algorithm ◽  
Distribution Network ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Network Reconfiguration ◽  
Swarm Optimization ◽  
Distribution Network Reconfiguration ◽  
Locality Property

This paper deals with the distribution network reconfiguration problem. A hybrid algorithm of particle swarm optimization (PSO) and tabu search (TS) is proposed as the searching algorithm. The new algorithm shares the advantages of PSO and TS, which has a fast computation speed and a strong ability to avoid local optimal solution. After a thorough comparison, network random key (NRK) is introduced as the corresponding coding strategy among various tree representation strategies. NRK could completely avoid the generation of infeasible solutions during the searching process and has a good locality property, which allows the new hybrid algorithm to perform to its fullest potential. The proposed algorithm has been validated through an IEEE 33 bus test case. Compared with other algorithms, the proposed method is both accurate and computationally efficient. Furthermore, a test to solve another problem also proves the robustness of the proposed algorithm for a different problem.

scholarly journals Handling Trust in a Cloud based Multi Agent System

2021 ◽  
Author(s):  
Imen Bouabdallah ◽  
Hakima Mellah
Keyword(s):  
Cloud Services ◽  
Distributed Network ◽  
Multi Agent System ◽  
It Infrastructure ◽  
Multi Agent Systems ◽  
Swarm Optimization ◽  
Agent Systems ◽  
Security Breaches ◽  
Ongoing Work ◽  
Multi Agent

Cloud computing is an opened and distributed network that guarantees access to a large amount of data and IT infrastructure at several levels (software, hardware...). With the increase demand, handling clients’ needs is getting increasingly challenging. Responding to all requesting clients could lead to security breaches, and since it is the provider’s responsibility to secure not only the offered cloud services but also the data, it is important to ensure clients reliability. Although filtering clients in the cloud is not so common, it is required to assure cloud safety. In this paper, by implementing multi agent systems in the cloud to handle interactions for the providers, trust is introduced at agent level to filtrate the clients asking for services by using Particle Swarm Optimization and acquaintance knowledge to determine malicious and untrustworthy clients. The selection depends on previous knowledge and overall rating of trusted peers. The conducted experiments show that the model outputs relevant results, and even with a small number of peers, the framework is able to converge to the best solution. The model presented in this paper is a part of ongoing work to adapt interactions in the cloud.

Sign in / Sign up

Export Citation Format

Share Document