Multi-agent Genetic Algorithm Research Articles

Research and Application of Emergency Logistics Resource Allocation Algorithm based on Supply Chain Network

The ”Fuzzy-Enhanced for Emergency Logistics Resource Allocation in Supply Chain Networks (FEM-ELRAS)” presents a novel approach to optimizing emergency logistics and resource allocation in supply chain networks, especially during critical disaster response scenarios. This research integrates fuzzy logic with the improve Multi Agent Genetic Algorithm (MAGA), creating a more adaptive and efficient framework capable of handling the uncertainties and complexities inherent in emergency situations. FEM-ELRAS employs fuzzy decision variables to represent ambiguous and fluctuating parameters like demand at disaster sites, supply availability, and variable transportation conditions. It incorporates a fuzzy inference system, utilizing expert-derived rules to guide the allocation process amidst uncertain and rapidly changing conditions. The algorithm’s evaluation mechanism is enhanced with fuzzy logic, offering a refined assessment of solution effectiveness, balancing multiple logistical objectives such as minimizing response time, optimizing costs, and maximizing resource utilization and delivery precision. Moreover, fuzzy logic principles are integrated into the genetic algorithm’s operators, enabling more context-sensitive and flexible solution adaptations. FEM-ELRAS is particularly designed to navigate the trade-offs between different logistical goals in emergency scenarios, making it a robust tool for decision-makers in disaster management. Its application promises significant improvements in emergency response efficiency, showcasing a step forward in the field of emergency logistics and supply chain management.

A novel multi-agent genetic algorithm for limited-view computed tomography

Computed tomography (CT) is a non-invasive testing technique to generate the internal image of any object. It has various applications belonging to medical imaging and other engineering applications. The present article proposes a novel multiagent genetic algorithm based on hill-climbing for limited-view computed tomography (MAGAH-CT). The MAGAH combines a multi-agent system based on a genetic algorithm (MAGA) and a hill-climbing approach. The MAGAH uses multiple operators to refine the agent population during evolution. These operators are neighborhood-based competition, hybrid crossover, adaptive mutation, and self-learning. MAGAH-CT explores better solutions in neighborhood-based competition and cooperation operators. The self-learning operator learns through the agent’s population generated by adaptive mutation and hybrid crossover operator, ensuring the results’ continuity. The proposed algorithm has been tested on a Shepp-Logan head phantom with limited view projection data. The algorithm can accurately reconstruct the low-noise CT image using limited data constraints. Experimental results reveal that the presented MAGAH-CT algorithm is efficient and computationally reasonable. The presented algorithm is suitable for low-noise CT reconstruction and out-performs other limited-view CT reconstruction algorithms.

An Effective Approach for Modular Community Detection in Bipartite Network Based on Integrating Rider with Harris Hawks Optimization Algorithms

The strenuous mining and arduous discovery of the concealed community structure in complex networks has received tremendous attention by the research community and is a trending domain in the multifaceted network as it not only reveals details about the hierarchical structure of multifaceted network but also assists in better understanding of the core functions of the network and subsequently information recommendation. The bipartite networks belong to the multifaceted network whose nodes can be divided into a dissimilar node-set so that no edges assist between the vertices. Even though the discovery of communities in one-mode network is briefly studied, community detection in bipartite networks is not studied. In this paper, we propose a novel Rider-Harris Hawks Optimization (RHHO) algorithm for community detection in a bipartite network through node similarity. The proposed RHHO is developed by the integration of the Rider Optimization (RO) algorithm with the Harris Hawks Optimization (HHO) algorithm. Moreover, a new evaluation metric, i.e., h-Tversky Index (h-TI), is also proposed for computing node similarity and fitness is newly devised considering modularity. The goal of modularity is to quantify the goodness of a specific division of network to evaluate the accuracy of the proposed community detection. The quantitative assessment of the proposed approach, as well as thorough comparative evaluation, was meticulously conducted in terms of fitness and modularity over the citation networks datasets (cit-HepPh and cit-HepTh) and bipartite network datasets (Movie Lens 100 K and American Revolution datasets). The performance was analyzed for 250 iterations of the simulation experiments. Experimental results have shown that the proposed method demonstrated a maximal fitness of 0.74353 and maximal modularity of 0.77433, outperforming the state-of-the-art approaches, including h-index-based link prediction, such as Multiagent Genetic Algorithm (MAGA), Genetic Algorithm (GA), Memetic Algorithm for Community Detection in Bipartite Networks (MATMCD-BN), and HHO.

Optimization Algorithm and Simulation of Public Resource Emergency Scheduling Based on Wireless Sensor Technology

Public resource scheduling refers to the rational allocation and effective use of resources, while public emergency scheduling refers to the rational allocation and effective use of resources in the context of emergencies. Its main purpose is to reduce casualties and property losses caused by emergencies. This paper mainly studies the emergency scheduling of public resources based on line sensing technology and solves the scheduling problem of public resources through algorithm optimization. Firstly, combined with the positioning algorithm of wireless sensor, this paper optimizes the positioning and detection technology of wireless sensor technology. Then, we design an improved multiagent genetic algorithm (MAGA‐MTERS) using natural number coding and design a penalty function to solve the model. Then, the algorithm is compared with the traditional genetic algorithm. The results show that the accurate positioning of wireless sensor technology can improve the efficiency of public resource scheduling and save the scheduling cost. The multiagent genetic algorithm optimizes the positioning function of wireless sensor. Compared with the traditional genetic algorithm, MAGA‐MTERS algorithm can obtain a better solution.

Synthesis of large‐scale thinned arrays based on a multiagent genetic algorithm

Advanced array synthesis problems are usually nonconvex regarding the cost functions, which are rather difficult to solve. A numerical stochastic optimization approach based on the multiagent genetic algorithm (MAGA) is proposed for the synthesis of highly thinned arrays in this article. In particular, two modified techniques are introduced into the traditional MAGA, named as improved MAGA (I-MAGA), to obtain the balance between exploration and exploitation ability. The proposed approach is utilized in some advanced antenna array synthesis problems, including the synthesis of sum and difference patterns and synthesis of several highly thinned planar arrays with different apertures. Numerical results indicate that the proposed I-MAGA has an attractive exploration and exploitation ability in the synthesis of highly thinned arrays. Besides, owing to the independent multiagent system, the I-MAGA is also able to obtain better solutions with a much smaller population size and less computational cost.

Multi-Agent Genetic Algorithm for Efficient Load Balancing in Cloud Computing

Cloud computing, one of the fastest growing fields, is the the delivery of computing resources and services. Load balancing is a key problem in cloud computing (CC) that deals with the even distribution of work load across multiple virtual machines to ensure that no machine is overloaded or underutilized during the task computation. The load balancing optimization problem is an NP-hard problem, hence, for the optimal usage of available resources, we propose a new efficient user-priority multi-agent genetic algorithm (GA). Our algorithm takes the “users’ priority and earliest job finishing time” into consideration for minimizing the response time and energy. We simulate our algorithm using Cloud-Analyst and show that our algorithm outperforms the existing algorithms for load balancing.

Multi-agent genetic algorithm based on fuzzy clustering in solving multi-objective optimization problems

Multi-agent genetic algorithm based on fuzzy clustering in solving multi-objective optimization problems

Research on multi-agent genetic algorithm based on tabu search for the job shop scheduling problem.

The solution to the job shop scheduling problem (JSSP) is of great significance for improving resource utilization and production efficiency of enterprises. In this paper, in view of its non-deterministic polynomial properties, a multi-agent genetic algorithm based on tabu search (MAGATS) is proposed to solve JSSPs under makespan constraints. Firstly, a multi-agent genetic algorithm (MAGA) is proposed. During the process, a multi-agent grid environment is constructed based on characteristics of multi-agent systems and genetic algorithm (GA), and a corresponding neighbor interaction operator, a mutation operator based on neighborhood structure and a self-learning operator are designed. Then, combining tabu search algorithm with a MAGA, the algorithm MAGATS are presented. Finally, 43 benchmark instances are tested with the new algorithm. Compared with four other algorithms, the optimization performance of it is analyzed based on obtained test results. Effectiveness of the new algorithm is verified by analysis results.

Learning fuzzy cognitive maps with convergence using a multi-agent genetic algorithm

Fuzzy cognitive maps (FCMs) are generally applied to model and analyze complex dynamical systems. Recently, many evolutionary-based algorithms are proposed to learn FCMs from historical data by optimizing Data_Error, which is used to evaluate the difference between available response sequences and generated response sequences. However, when Data_Error is adopted as the fitness function for learning FCMs, two problems arise. One is that the optimization reaches the desired result slowly; the other is that the learned FCMs have high link density. To solve these problems, we propose another objective named as convergence error, which is inspired by the convergence of FCMs, to evaluate the difference between the convergent value of available response sequences and that of generated response sequences. In addition, a multi-agent genetic algorithm (MAGA), which is effective for large-scale global numerical optimization, is adopted to optimize convergence error for learning FCMs. To this end, a novel learning approach, a multi-agent genetic algorithm based on the convergence error (MAGA-Convergence), is proposed for learning FCMs. MAGA-Convergence needs less data, because the only initial value and convergent value of the available response sequences are needed for learning FCMs. In the experiments, MAGA-Convergence is applied to learn the FCMs of synthetic data and the benchmarks DREAM3 and DREAM4 for gene regulatory network reconstruction. The experimental results show that the learned FCMs are sparse and could be learned in much fewer generations than other learning algorithms.

Designing comprehensively robust networks against intentional attacks and cascading failures

Networked systems exist broadly in daily life and have been greatly emphasized in recent studies. Since these networks are always exposed in complicated environments and may become damaged from different kinds of attacks and failures, including intentional attacks and cascading failures, many researchers have studied the models and mitigation strategies towards these two issues separately. As has been indicated by some reports and materials, the design of comprehensively robust networks against both intentional attacks and cascading failures is of significance in daily life but existing optimization methods seem to be helpless when dealing with this problem. Therefore, this paper focuses on designing such networks. A normalized measure is first designed to evaluate the comprehensive robustness of networks. Then, a multi-agent genetic algorithm is devised to search for robust structures initialized by an input network, termed MAGA-NetR. Combining local and global optima information, the proposed algorithm is validated to be effective on synthetic and real-world networks and to outperform existing optimization methods. The optimal network designed by the proposed algorithm could facilitate theoretical analyses on networked systems and may provide potential candidates to solve some realistic dilemmas.

Learning of fuzzy cognitive maps using a niching-based multi-modal multi-agent genetic algorithm

Fuzzy cognitive maps (FCMs) are generally applied to model and analyze complex dynamical systems. To learn the FCM weight matrix, various efficient learning algorithms have been proposed. However, those algorithms only learn one FCM from data once. Learning only one FCM is not enough for modeling and analyzing complex dynamical systems because the learned FCM may be just a local optimum. To solve this problem, we tend to learn multiple FCMs simultaneously. To this end, the FCM learning problem is modeled as a multi-modal optimization problem. So far, niching is the most adopted method to deal with multi-modal optimization. Thus, a multi-agent genetic algorithm (MAGA), which is a popular numerical optimization algorithm, is combined with current niching methods. Then, a niching-based multi-modal multi-agent genetic algorithm is proposed for learning FCM, termed as NMMMAGA-FCM. In this paper, NMMMAGA-FCM is adopted to learn several FCMs at the same time, then chooses the optimal FCM from all candidates. In the experiments, NMMMAGA-FCM is applied to learn the FCMs from synthetic data with varying sizes and densities. The experimental results show that NMMMAGA-FCM can learn FCMs with high accuracy. In addition, NMMMAGA-FCM is validated on the benchmark datasets DREAM3 and DREAM4. The experimental results show that NMMMAGA-FCM outperforms other learning algorithms obviously, which illustrates that NMMMAGA-FCM can reconstruct gene regulatory networks (GRNs) effectively.

Inferring gene regulatory networks with hybrid of multi-agent genetic algorithm and random forests based on fuzzy cognitive maps

Inferring gene regulatory networks (GRNs) from expression data is an important and challenging problem in the field of computational biology. With the growth of high-throughput gene expression data, GRN inference has attracted much interest from researchers. In this paper, we focus on inferring large-scale GRNs using a fast and accurate algorithm. We first use fuzzy cognitive maps (FCMs) to model GRNs. Then, multi-agent genetic algorithm (MAGA) is used to determine regulatory links, and random forests (RF) are used as the feature selection algorithm to initialize the agents, which can reduce the search space of MAGA according to the gene ranking. We improve the genetic operators of MAGA to cope with GRN inference. The proposed algorithm is termed as MAGARFFCM-GRN. In the experiments, the performance of MAGARFFCM-GRN is validated on synthetic data and the well-known benchmark DREAM3 and DREAM4. The results show that MAGARFFCM-GRN can infer directed GRNs with high accuracy and efficiency.

Multi-agent genetic algorithm with controllable mutation probability utilizing back propagation neural network for global optimization of trajectory design

A Controllable Mutation Probability (CMP) strategy is proposed and applied to a Multi-Agent Genetic Algorithm (MAGA) to deal with the global optimization of trajectory design in deep space, which is called MGA-CMP. MAGA-CMP is an algorithm setting all the individuals (or agents) on a grid and having two controlling functions to adjust the performance probability of a mutation operator. It pays more attention to global search in the earlier part of the process, and devotes more effort to local search at later stages. Four experiments are implemented to illustrate the efficiency of MAGA-CMP, and results show that MGA-CMP performs better in most examined cases than other well-known search algorithms.

A time series driven decomposed evolutionary optimization approach for reconstructing large-scale gene regulatory networks based on fuzzy cognitive maps

BackgroundReconstructing gene regulatory networks (GRNs) from expression data plays an important role in understanding the fundamental cellular processes and revealing the underlying relations among genes. Although many algorithms have been proposed to reconstruct GRNs, more rapid and efficient methods which can handle large-scale problems still need to be developed. The process of reconstructing GRNs can be formulated as an optimization problem, which is actually reconstructing GRNs from time series data, and the reconstructed GRNs have good ability to simulate the observed time series. This is a typical big optimization problem, since the number of variables needs to be optimized increases quadratically with the scale of GRNs, resulting an exponential increase in the number of candidate solutions. Thus, there is a legitimate need to devise methods capable of automatically reconstructing large-scale GRNs.ResultsIn this paper, we use fuzzy cognitive maps (FCMs) to model GRNs, in which each node of FCMs represent a single gene. However, most of the current training algorithms for FCMs are only able to train FCMs with dozens of nodes. Here, a new evolutionary algorithm is proposed to train FCMs, which combines a dynamical multi-agent genetic algorithm (dMAGA) with the decomposition-based model, and termed as dMAGA-FCMD, which is able to deal with large-scale FCMs with up to 500 nodes. Both large-scale synthetic FCMs and the benchmark DREAM4 for reconstructing biological GRNs are used in the experiments to validate the performance of dMAGA-FCMD.ConclusionsThe dMAGA-FCMD is compared with the other four algorithms which are all state-of-the-art FCM training algorithms, and the results show that the dMAGA-FCMD performs the best. In addition, the experimental results on FCMs with 500 nodes and DREAM4 project demonstrate that dMAGA-FCMD is capable of effectively and computationally efficiently training large-scale FCMs and GRNs.

Hybrid multiagent genetic algorithm for job shop scheduling problem

Hybrid multiagent genetic algorithm for job shop scheduling problem

A Dynamic Multiagent Genetic Algorithm for Gene Regulatory Network Reconstruction Based on Fuzzy Cognitive Maps

In order to reconstruct large-scale gene regulatory networks (GRNs) with high accuracy, a robust evolutionary algorithm, a dynamic multiagent genetic algorithm (dMAGA), is proposed to reconstruct GRNs from time-series expression profiles based on fuzzy cognitive maps (FCMs) in this paper. The algorithm is labeled as dMAGAFCM-GRN. In dMAGAFCM-GRN, agents and their behaviors are designed with the intrinsic properties of GRN reconstruction problems in mind. All agents live in a lattice-like environment, and the neighbors of each agent are changed dynamically according to their energy in each generation. dMAGAFCM-GRN can learn continuous states directly for FCMs from data. In the experiments, the performance of dMAGAFCM-GRN is validated on both large-scale synthetic data and the benchmark DREAM3 and DREAM4. The experimental results show that dMAGAFCM-GRN is able to effectively learn FCMs with 200 nodes; that is, 40 000 weights need to be optimized. The systematic comparison with five existing algorithms shows that dMAGAFCM-GRN outperforms all other algorithms and can approximate the time series with high accuracy.

A multi-agent genetic algorithm for community detection in complex networks

Complex networks are popularly used to represent a lot of practical systems in the domains of biology and sociology, and the structure of community is one of the most important network attributes which has received an enormous amount of attention. Community detection is the process of discovering the community structure hidden in complex networks, and modularity Q is one of the best known quality functions measuring the quality of communities of networks. In this paper, a multi-agent genetic algorithm, named as MAGA-Net, is proposed to optimize modularity value for the community detection. An agent, coded by a division of a network, represents a candidate solution. All agents live in a lattice-like environment, with each agent fixed on a lattice point. A series of operators are designed, namely split and merging based neighborhood competition operator, hybrid neighborhood crossover, adaptive mutation and self-learning operator, to increase modularity value. In the experiments, the performance of MAGA-Net is validated on both well-known real-world benchmark networks and large-scale synthetic LFR networks with 5000 nodes. The systematic comparisons with GA-Net and Meme-Net show that MAGA-Net outperforms these two algorithms, and can detect communities with high speed, accuracy and stability.

Multiagent Genetic Algorithm: An Online Probabilistic View on Economic Dispatch of Energy Hubs Constrained by Wind Availability

Multiple energy carriers (MECs) have been broadly engrossing power system planners and operators toward a modern standpoint in power system studies. Energy hub, though playing an undeniable role as the intermediate in implementing the MECs, still needs to be put under examination in both modeling and operating concerns. Since wind power continues to be one of the fastest-growing energy resources worldwide, its intrinsic challenges should be also treated as an element of crucial role in the vision of future energy networks. In response, this paper aims to concentrate on the online economic dispatch (ED) of MECs for which it provides a probabilistic ED optimization model. The presented model is treated via a robust optimization technique, i.e., multiagent genetic algorithm (MAGA), whose outstanding feature is to find well the global optima of the ED problem. ED once constrained by wind power availability, in the cases of wind power as one of the input energy carriers of the hub, faces an inevitable uncertainty that is also probabilistically overcome in the proposed model. Efficiently approached via MAGA, the presented scheme is applied to test systems equipped with energy hubs and as expected, introduces its applicability and robustness in the ED problems.

Multi-Resolution Dijkstra Method Based on Multi-Agent Simulation and its Application to Genetic Algorithm for Classroom Optimization

The combinatorial optimization problem of university classroom schedule assignments is formulated using multiagent simulation and genetic algorithms in the evaluation and optimization process. The method we propose consists of global and local multiagent planning. Conventional global planning requires setting subgoals manually, which became a bottleneck in optimization. To solve this problem, a multi-resolution Dijkstra method for selected autonomously, assuming eight classrooms as a real University of Tsukuba building and 250 agents, we confirmed the effectiveness of the proposed multi-resolution Dijkstra’s algorithm as for both global and local route selections, compared to the uniform Dijkstra’s method.

A Decomposed Solution to Multiple-Energy Carriers Optimal Power Flow

Presence of energy hubs in the future vision of energy networks creates a great opportunity for system planners and operators to move towards more efficient systems. The role of energy hubs as the intermediate in multi-carrier energy (MCE) systems calls for a generic framework to study the new upcoming technical as well as economical effects on the system performance. In response, this paper attempts to develop a general optimization and modeling framework for coupled power flow studies on different energy infrastructures. This, as a large-scale nonlinear problem, is approached through a robust optimization technique, i.e., multi-agent genetic algorithm (MAGA). The proposed procedure decomposes the multi-carrier optimal power flow (MCOPF) problem into its traditional separate OPF problem in such a way that the major advantages of simultaneous analysis of MCE systems would not be sacrificed. The presented scheme is then applied to an 11-hubs test system and introduces its expected applicability and robustness in the MCE systems analysis.