scholarly journals AntNet: Distributed Stigmergetic Control for Communications Networks

1998 ◽  
Vol 9 ◽  
pp. 317-365 ◽  
Author(s):  
G. Di Caro ◽  
M. Dorigo
Keyword(s):  
Adaptive Learning ◽  
Mobile Agents ◽  
Optimization Problems ◽  
State Of The Art ◽  
Routing Algorithms ◽  
Superior Performance ◽  
Experimental Conditions ◽  
Communications Networks ◽  
Novel Approach ◽  
Routing Tables

This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.

A Novel Approach to Designing Surrogate-assisted Genetic Algorithms by Combining Efficient Learning of Walsh Coefficients and Dependencies

2021 ◽  
Vol 1 (2) ◽  
pp. 1-23
Author(s):  
Arkadiy Dushatskiy ◽  
Tanja Alderliesten ◽  
Peter A. N. Bosman
Keyword(s):  
Boolean Functions ◽  
Surrogate Model ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fitness Function ◽  
Optimal Solution ◽  
Problem Structure ◽  
Novel Approach ◽  
Efficient Learning ◽  
Nk Landscapes

Surrogate-assisted evolutionary algorithms have the potential to be of high value for real-world optimization problems when fitness evaluations are expensive, limiting the number of evaluations that can be performed. In this article, we consider the domain of pseudo-Boolean functions in a black-box setting. Moreover, instead of using a surrogate model as an approximation of a fitness function, we propose to precisely learn the coefficients of the Walsh decomposition of a fitness function and use the Walsh decomposition as a surrogate. If the coefficients are learned correctly, then the Walsh decomposition values perfectly match with the fitness function, and, thus, the optimal solution to the problem can be found by optimizing the surrogate without any additional evaluations of the original fitness function. It is known that the Walsh coefficients can be efficiently learned for pseudo-Boolean functions with k -bounded epistasis and known problem structure. We propose to learn dependencies between variables first and, therefore, substantially reduce the number of Walsh coefficients to be calculated. After the accurate Walsh decomposition is obtained, the surrogate model is optimized using GOMEA, which is considered to be a state-of-the-art binary optimization algorithm. We compare the proposed approach with standard GOMEA and two other Walsh decomposition-based algorithms. The benchmark functions in the experiments are well-known trap functions, NK-landscapes, MaxCut, and MAX3SAT problems. The experimental results demonstrate that the proposed approach is scalable at the supposed complexity of O (ℓ log ℓ) function evaluations when the number of subfunctions is O (ℓ) and all subfunctions are k -bounded, outperforming all considered algorithms.

scholarly journals Antnet: A Robust Routing Algorithm for Data Networks

1970 ◽  
Vol 5 (1) ◽  
Author(s):  
Shariq Haseeb Khairul Azami Sidek Ahmad Faris Ismail, Lai W.K. ◽  
Aw Yit Mei
Keyword(s):  
Mobile Agents ◽  
Routing Algorithm ◽  
Routing Algorithms ◽  
Convergence Time ◽  
Data Networks ◽  
Successful Implementation ◽  
Link State ◽  
Performance Matrix ◽  
Telecommunications Network ◽  
Routing Tables

Successful implementation and operation of a network largely depends on the routing algorithm in use. To date, several routing algorithms are in use but the problem with these algorithms is that they are either not adaptive or not robust enough, thus limiting the proper use of bandwidth.  AntNet is an innovative algorithm that may be used for data networks. It is a combination of both static and dynamic routing algorithms. In this algorithm, a group of mobile agents (compared to real ants) form paths between source and destination nodes. They explore the network continuously and exchange obtained information indirectly, in order to update the routing tables at different nodes. Our version of AntNet (hereinafter referred to as AntNet2.0) has been improved to overcome the problems with other algorithms. This paper compares the performance of AntNet2.0 against two other commercially popular algorithms, viz. link state routing algorithm and distant vector routing algorithm. The performance matrix used to compare the algorithms is based on average throughput, packet loss, packet drop and end-to-end delay. Convergence time for this algorithm on a nation-wide telecommunications network will also be discussed. Conclusions and areas of further work will also be presented in lucid manner, so that it may be transformed into real practice in the future.Key Words: mobile agents, swarm intelligence, networks and constant bit rate

scholarly journals Intelligent Rapid Adaptive Offloading Algorithm for Computational Services in Dynamic Internet of Things System

Sensors ◽  
2019 ◽  
Vol 19 (15) ◽  
pp. 3423 ◽  
Author(s):  
Li ◽  
Qin ◽  
Zhou ◽  
Cheng ◽  
Zhang ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Adaptive Learning ◽  
Optimization Problems ◽  
Estimation Algorithm ◽  
Superior Performance ◽  
Innovative Technology ◽  
Centralized Control ◽  
Asynchronous Sampling ◽  
Computational Performance ◽  
Experience Replay

As restricted resources have seriously limited the computational performance of massive Internet of things (IoT) devices, better processing capability is urgently required. As an innovative technology, multi-access edge computing can provide cloudlet capabilities by offloading computation-intensive services from devices to a nearby edge server. This paper proposes an intelligent rapid adaptive offloading (IRAO) algorithm for a dynamic IoT system to increase overall computational performance and simultaneously keep the fairness of multiple participants, which can achieve agile centralized control and solve the joint optimization problems related to offloading policy and resource allocation. For reducing algorithm execution time, we apply machine learning methods and construct an adaptive learning-based framework consisting of offloading decision-making, radio resource slicing and algorithm parameters updating. In particular, the offloading policy can be rapidly derived from an estimation algorithm based on a deep neural network, which uses an experience replay training method to improve model accuracy and adopts an asynchronous sampling trick to enhance training convergence performance. Extensive simulations with different parameters are conducted to maintain the trade-off between accuracy and efficiency of the IRAO algorithm. Compared with other candidates, the results illustrate that the IRAO algorithm can achieve superior performance in terms of scalability, effectiveness and efficiency.

RANDOMIZED MOBILE AGENT BASED ROUTING IN WIRELESS NETWORKS

2001 ◽  
Vol 12 (03) ◽  
pp. 365-384 ◽  
Author(s):  
MARC BUI ◽  
SAJAL K. DAS ◽  
AJOY K. DATTA ◽  
DAI THO NGUYEN
Keyword(s):  
Mobile Agent ◽  
Mobile Networks ◽  
Mobile Agents ◽  
Shortest Paths ◽  
Mobile Nodes ◽  
Shortest Path Routing ◽  
Link State ◽  
Novel Approach ◽  
Link Cost ◽  
Routing Tables

We propose a novel approach for shortest path routing in wireless mobile networks. The approach makes use of n mobile agents initially launched from n mobile nodes forming the network. The agents move randomly from node to node and update routing information as they go. The approach is presented in this paper with two protocols. Both of them exhibit good performance in terms of the network and computing resource consumptions. The first protocol relies on independent mobile agents and imposes a minimum bandwidth requirement on individual mobile agents. Each agent carries the link state of its creator and this information remains unchanged except when the mobile agent returns to the home node. The second protocol is a refinement of the first protocol, with some form of interaction between the mobile agents. Each agent maintains the routing table of its creator instead of link state. The randomly walking agents spread the update information and compute the shortest paths via exchanging network state information between the routing tables they carry and the routing tables at the nodes they traverse. The correctness of the protocols is proven. Our analysis shows that the agent cooperation improves the system performance when dealing with topology and link cost changes.

An evolutionary based framework for many-objective optimization problems

2018 ◽  
Vol 35 (4) ◽  
pp. 1805-1828 ◽  
Author(s):  
Kimia Bazargan Lari ◽  
Ali Hamzeh
Keyword(s):  
Optimization Problems ◽  
State Of The Art ◽  
Fitness Function ◽  
Population Diversity ◽  
Numerical Results ◽  
Superior Performance ◽  
High Dimensional ◽  
Test Problems ◽  
Content Type ◽  
Objective Space

Purpose Recently, many-objective optimization evolutionary algorithms have been the main issue for researchers in the multi-objective optimization community. To deal with many-objective problems (typically for four or more objectives) some modern frameworks are proposed which have the potential of achieving the finest non-dominated solutions in many-objective spaces. The effectiveness of these algorithms deteriorates greatly as the problem’s dimension increases. Diversity reduction in the objective space is the main reason of this phenomenon. Design/methodology/approach To properly deal with this undesirable situation, this work introduces an indicator-based evolutionary framework that can preserve the population diversity by producing a set of discriminated solutions in high-dimensional objective space. This work attempts to diversify the objective space by proposing a fitness function capable of discriminating the chromosomes in high-dimensional space. The numerical results prove the potential of the proposed method, which had superior performance in most of test problems in comparison with state-of-the-art algorithms. Findings The achieved numerical results empirically prove the superiority of the proposed method to state-of-the-art counterparts in the most test problems of a known artificial benchmark. Originality/value This paper provides a new interpretation and important insights into the many-objective optimization realm by emphasizing on preserving the population diversity.

scholarly journals A novel approach for solving travelling thief problem using enhanced simulated annealing

2021 ◽  
Vol 7 ◽  
pp. e377
Author(s):  
Hamid Ali ◽  
Muhammad Zaid Rafique ◽  
Muhammad Shahzad Sarfraz ◽  
Muhammad Sheraz Arshad Malik ◽  
Mohammed A. Alqahtani ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Optimization Problem ◽  
Optimization Problems ◽  
State Of The Art ◽  
Minimum Cost ◽  
Medium Size ◽  
Time Interval ◽  
Maximum Profit ◽  
Novel Approach ◽  
Hard Problems

Real-world optimization problems are getting more and more complex due to the involvement of inter dependencies. These complex problems need more advanced optimizing techniques. The Traveling Thief Problem (TTP) is an optimization problem that combines two well-known NP-Hard problems including the 0/1 knapsack problem and traveling salesman problem. TTP contains a person known as a thief who plans a tour to collect multiple items to fill his knapsack to gain maximum profit while incurring minimum cost in a standard time interval of 600 s. This paper proposed an efficient technique to solve the TTP problem by rearranging the steps of the knapsack. Initially, the picking strategy starts randomly and then a traversal plan is generated through the Lin-Kernighan heuristic. This traversal is then improved by eliminating the insignificant cities which contribute towards profit adversely by applying the modified simulated annealing technique. The proposed technique on different instances shows promising results as compared to other state-of-the-art algorithms. This technique has outperformed on a small and medium-size instance and competitive results have been obtained in the context of relatively larger instances.

scholarly journals Structured Clanning-Based Ensemble Optimization Algorithm: A Novel Approach for Solving Complex Numerical Problems

2018 ◽  
Vol 2018 ◽  
pp. 1-19
Author(s):  
Avinash Sharma ◽  
Rajesh Kumar ◽  
Akash Saxena ◽  
B. K. Panigrahi
Keyword(s):  
Social Structure ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Numerical Optimization ◽  
Optimization Problems ◽  
State Of The Art ◽  
Algorithm Performance ◽  
Novel Approach ◽  
Ensemble Optimization ◽  
Metaheuristic Optimization Algorithm

In this paper, a novel swarm intelligence-based ensemble metaheuristic optimization algorithm, called Structured Clanning-based Ensemble Optimization, is proposed for solving complex numerical optimization problems. The proposed algorithm is inspired by the complex and diversified behaviour present within the fission-fusion-based social structure of the elephant society. The population of elephants can consist of various groups with relationship between individuals ranging from mother-child bond, bond groups, independent males, and strangers. The algorithm tries to model this individualistic behaviour to formulate an ensemble-based optimization algorithm. To test the efficiency and utility of the proposed algorithm, various benchmark functions of different geometric properties are used. The algorithm performance on these test benchmarks is compared to various state-of-the-art optimization algorithms. Experiments clearly showcase the success of the proposed algorithm in optimizing the benchmark functions to better values.

scholarly journals Multi-Label Classification Neural Networks with Hard Logical Constraints

2021 ◽  
Vol 72 ◽  
pp. 759-818
Author(s):  
Eleonora Giunchiglia ◽  
Thomas Lukasiewicz
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Experimental Analysis ◽  
State Of The Art ◽  
Superior Performance ◽  
Learning Problem ◽  
Improve Performance ◽  
Hard Constraints ◽  
Novel Approach ◽  
Logical Constraints

Multi-label classification (MC) is a standard machine learning problem in which a data point can be associated with a set of classes. A more challenging scenario is given by hierarchical multi-label classification (HMC) problems, in which every prediction must satisfy a given set of hard constraints expressing subclass relationships between classes. In this article, we propose C-HMCNN(h), a novel approach for solving HMC problems, which, given a network h for the underlying MC problem, exploits the hierarchy information in order to produce predictions coherent with the constraints and to improve performance. Furthermore, we extend the logic used to express HMC constraints in order to be able to specify more complex relations among the classes and propose a new model CCN(h), which extends C-HMCNN(h) and is again able to satisfy and exploit the constraints to improve performance. We conduct an extensive experimental analysis showing the superior performance of both C-HMCNN(h) and CCN(h) when compared to state-of-the-art models in both the HMC and the general MC setting with hard logical constraints.

An Adaptive Aggregation-Based Approach for Expensively Constrained Black-Box Optimization Problems

2018 ◽  
Vol 140 (9) ◽  
Author(s):  
George H. Cheng ◽  
Timothy Gjernes ◽  
G. Gary Wang
Keyword(s):  
Design Optimization ◽  
Real World ◽  
Optimization Problems ◽  
Trust Region ◽  
Adaptive Strategy ◽  
Black Box ◽  
Superior Performance ◽  
Adaptive Aggregation ◽  
Novel Approach ◽  
Single Objective

Expensive constraints are commonly seen in real-world engineering design. However, metamodel based design optimization (MBDO) approaches often assume inexpensive constraints. In this work, the situational adaptive Kreisselmeier and Steinhauser (SAKS) method was employed in the development of a hybrid adaptive aggregation-based constraint handling strategy for expensive black-box constraint functions. The SAKS method is a novel approach that hybridizes the modeling and aggregation of expensive constraints and adds an adaptive strategy to control the level of hybridization. The SAKS strategy was integrated with a modified trust region-based mode pursuing sampling (TRMPS) algorithm to form the SAKS-trust region optimizer (SAKS-TRO) for single-objective design optimization problems with expensive black-box objective and constraint functions. SAKS-TRO was benchmarked against five popular constrained optimizers and demonstrated superior performance on average. SAKS-TRO was also applied to optimize the design of an industrial recessed impeller.

scholarly journals Temporal Patternization of Power Signatures for Appliance Classification in NILM

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2931
Author(s):  
Hwan Kim ◽  
Sungsu Lim
Keyword(s):  
Deep Learning ◽  
Energy Demand ◽  
State Of The Art ◽  
Performance Comparison ◽  
Superior Performance ◽  
Total Power ◽  
Novel Approach ◽  
Load Monitoring ◽  
The Uk ◽  
Domestic Appliance

Non-Intrusive Load Monitoring (NILM) techniques are effective for managing energy and for addressing imbalances between the energy demand and supply. Various studies based on deep learning have reported the classification of appliances from aggregated power signals. In this paper, we propose a novel approach called a temporal bar graph, which patternizes the operational status of the appliances and time in order to extract the inherent features from the aggregated power signals for efficient load identification. To verify the effectiveness of the proposed method, a temporal bar graph was applied to the total power and tested on three state-of-the-art deep learning techniques that previously exhibited superior performance in image classification tasks—namely, Extreme Inception (Xception), Very Deep One Dimensional CNN (VDOCNN), and Concatenate-DenseNet121. The UK Domestic Appliance-Level Electricity (UK-DALE) and Tracebase datasets were used for our experiments. The results of the five-appliance case demonstrated that the accuracy and F1-score increased by 19.55% and 21.43%, respectively, on VDOCNN, and by 33.22% and 35.71%, respectively, on Xception. A performance comparison with the state-of-the-art deep learning methods and image-based spectrogram approach was conducted.

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