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 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-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.

Guaranteed cost consensus for high-dimensional multi-agent systems with time-varying delays

2018 ◽  
Vol 5 (1) ◽  
pp. 181-189 ◽  
Author(s):  
Zhong Wang ◽  
Ming He ◽  
Tang Zheng ◽  
Zhiliang Fan ◽  
Guangbin Liu
Keyword(s):  
High Dimensional ◽  
Time Varying ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Guaranteed Cost ◽  
Multi Agent

A Hybrid Multi-Agent-Based BFPSO Algorithm for Optimization of Benchmark Functions

2019 ◽  
Vol 29 (07) ◽  
pp. 2050112
Author(s):  
Renuka Kamdar ◽  
Priyanka Paliwal ◽  
Yogendra Kumar
Keyword(s):  
Optimization Problems ◽  
Learning Algorithm ◽  
Optimal Solution ◽  
System Optimization ◽  
High Dimensional ◽  
Potential Candidate ◽  
Optimization Strategy ◽  
Multi Agent Systems ◽  
Agent Based ◽  
Multi Agent

The goal to provide faster and optimal solution to complex and high-dimensional problem is pushing the technical envelope related to new algorithms. While many approaches use centralized strategies, the concept of multi-agent systems (MASS) is creating a new option related to distributed analyses for the optimization problems. A novel learning algorithm for solving the global numerical optimization problems is proposed. The proposed learning algorithm integrates the multi-agent system and the hybrid butterfly–particle swarm optimization (BFPSO) algorithm. Thus it is named as multi-agent-based BFPSO (MABFPSO). In order to obtain the optimal solution quickly, each agent competes and cooperates with its neighbors and it can also learn by using its knowledge. Making use of these agent–agent interactions and sensitivity and probability mechanism of BFPSO, MABFPSO realizes the purpose of optimizing the value of objective function. The designed MABFPSO algorithm is tested on specific benchmark functions. Simulations of the proposed algorithm have been performed for the optimization of functions of 2, 20 and 30 dimensions. The comparative simulation results with conventional PSO approaches demonstrate that the proposed algorithm is a potential candidate for optimization of both low-and high-dimensional functions. The optimization strategy is general and can be used to solve other power system optimization problems as well.

Sign in / Sign up

Export Citation Format

Share Document