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bio-inspired deep residual neural network learning model for QoS routing enhancement in mobile ad-hoc networks

WIRELESS NETWORKS 2023年第8期29卷 3541-3565页

作者： Tamizharasi, S. Arunadevi, B. Deepa, S. N. RVS Coll Engn & Technol Dept Elect & Commun Engn Coimbatore 641402 India Dr NGP Inst Technol Dept Elect & Commun Engn Coimbatore 641048 India Natl Inst Technol Arunachal Pradesh Dept Elect Engn Jote 791113 Arunachal Prade India

In mobile ad-hoc networks (MANETs) always the quality-of-service (QoS) routing problem remains to exist and as a non-deterministic polynomial hard problem it is necessary to improve the QoS parameters to the most possible extent. For the considered MANET model, it is important to develop a suitable model that is capable of improving and enhancing the quality-of-service metrics. In this research study, a novel bio-inspired deep residual neural network (DResNet) architecture algorithm is developed over a MANET model for designing a most effective QoS routing protocol. The main aim of this proposed study is to locate a better routing path with satisfied QoS metrics for the MANET model and the bio-inspired deep learning neural model gets trained to meet the set fitness function. The new DResNet architecture operates with limited quantity of training data and its weights are optimized with novel IIWGSO technique. The proposed IIWGSO based DResNet model with the ad-hoc on-demand distance vector (AODV) protocol gets trained for the MANET model and its effectiveness is justified with QoS constraints being satisfied. The new IIWGSO-DResNet-AODV architecture resulted in better fitness value and QoS metrics proving its superiority compared to the existing techniques from previous literatures for the same MANET model.

关键词： MANET model Quality-of-service Deep learning Deep residual neural network Routing bio-inspired algorithm AODV protocol

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New directional bat algorithm for continuous optimization problems

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 69卷 159-175页

作者： Chakri, Asma Khelif, Rabia Benouaret, Mohamed Yang, Xin-She Univ Badji Mokhtar Annaba Dept Mech Engn Ind Mech Lab BP12-23000 Annaba Algeria Middlesex Univ London Sch Sci & Technol London NW4 4BT England

Bat algorithm (BA) is a recent optimization algorithm based on swarm intelligence and inspiration from the echolocation behavior of bats. One of the issues in the standard bat algorithm is the premature convergence that can occur due to the low exploration ability of the algorithm under some conditions. To overcome this deficiency, directional echolocation is introduced to the standard bat algorithm to enhance its exploration and exploitation capabilities. In addition to such directional echolocation, three other improvements have been embedded into the standard bat algorithm to enhance its performance. The new proposed approach, namely the directional Bat algorithm (dBA), has been then tested using several standard and non-standard benchmarks from the CEC'2005 benchmark suite. The performance of dBA has been compared with ten other algorithms and BA variants using non-parametric statistical tests. The statistical test results show the superiority of the directional bat algorithm. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： bio-inspired algorithm Directional bat algorithm Optimization Unconstrained optimization Swarm intelligence

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Enhanced sooty tern optimization algorithm using multiple search guidance strategies and multiple position update modes for solving optimization problems

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APPLIED INTELLIGENCE 2023年第6期53卷 6763-6799页

作者： He, Jieguang Peng, Zhiping Cui, Delong Qiu, Jingbo Li, Qirui Zhang, Hao Guangdong Univ Petrochem Technol Coll Comp Sci Maoming Peoples R China Zhejiang Univ State Key Lab Ind Control Technol Hangzhou Peoples R China Jiangmen Polytech Sch Informat Engn Jiangmen Peoples R China Guangdong Univ Petrochem Technol Guangdong Prov Key Lab Petrochem Equipment Fault Maoming Peoples R China Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai Peoples R China

The Sooty Tern Optimization algorithm (STOA) is a newly proposed bio-inspired algorithm that mimics the migration and attacking behaviors of the sea bird sooty tern in nature. STOA has several excellent advantages, including fewer parameters, a simple structure, a fast convergence rate, and high exploitation. Nevertheless, it is difficult to find the global optimal solution and prone to losing population diversity when dealing with complex optimization problems due to its single search guidance strategy and position update method. An enhanced STOA (ESTOA) is proposed to address these shortcomings that incorporates multiple search guidance strategies and position update modes. In terms of search guidance, in addition to the best individual in the original STOA, the mean individual and a randomly selected individual are also designed to guide the search. Six position update modes are proposed in conjunction with the guidance strategies, including one improved scaling mode with an extended spiral radius and five other modes based on offset operations. Due to their distinct design objectives, these guidance strategies and position update modes exhibit varying levels of search intensity and optimization effect. However, they complement one another and work cooperatively to achieve a good balance of global exploration and local exploitation. Several widely used sets of benchmark functions with a wide range of dimensions and varying degrees of complexity are used to validate ESTOA's performance. The obtained results are compared to those of other state-of-the-art optimization algorithms in terms of convergence accuracy and a variety of numerical performance evaluation parameters. A significant improvement in solution quality demonstrates that ESTOA can increase population diversity and maintain a good balance between global exploring and local exploiting abilities.

关键词： Sooty Tern Optimization algorithm bio-inspired algorithm Optimization Search guidance Position update

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PSO-Based Dynamic UAV Positioning algorithm for Sensing Information Acquisition in Wireless Sensor Networks

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IEEE ACCESS 2019年 7卷 77499-77513页

作者： Na, Ho Jeong Yoo, Sang-Jo Inha Univ Dept Informat & Commun Engn Jncheon 402751 South Korea

In this paper, we propose a dynamic unmanned aerial vehicle (UAV) positioning method to maximize the value of the sensor data information acquired from multiple UAVs in wireless sensor networks. In operations of UAVs to monitor environmental disasters or perform military missions, the value of the acquired sensing information depends on the sensor types and the elapsed time after the previous sensing time. To support real-time sensor data monitoring, the sensed data should be successfully delivered to the ground base station (or sink node) using a UAV flying ad-hoc network;herein, it is important to maintain the connectivity between the UAVs and to consider the reliability of the communication links. In this paper, particle swarm optimization (PSO) is used to derive optimum UAV locations. For a specified wireless sensor network, at each time instance, new UAV locations are updated that guarantee complete connectivity of the UAVs and maximize the value of the aggregated sensor data information. In this paper, we have formulated the UAV location search model as a constrained optimization problem with multi-objective utility functions using PSO-based bio-inspired algorithm. The simulation results demonstrate that by maximizing the intended multi-objective utility function, the proposed method can dynamically derive the optimal locations of multiple UAVs and achieve better sensing information acquisition compared to other methods.

关键词： UAV wireless sensor networks positioning bio-inspired algorithm particle swarm optimization

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Grey Wolf optimization-Elman neural network model for stock price prediction

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SOFT COMPUTING 2021年第1期25卷 649-658页

作者： Kumar Chandar, S. CHRIST Deemed Univ Sch Business & Management Bangalore Karnataka India

Over the past two decades, assessing future price of stock market has been a very active area of research in financial world. Stock price always fluctuates due to many variables. Thus, an accurate prediction of stock price can be considered as a tough task. This study intends to design an efficient model for predicting future price of stock market using technical indicators derived from historical data and natural inspired algorithm. The model adopts Elman neural network (ENN) because of its ability to memorize the past information, which is suitable for solving stock problems. Trial and error-based method is widely used to determine the parameters of ENN. It is a time-consuming task. To address such an issue, this study employs Grey Wolf optimization (GWO) algorithm to optimize the parameters of ENN. Optimized ENN is utilized to predict the future price of stock data in 1 day advance. To evaluate the prediction efficiency, proposed model is tested on NYSE and NASDAQ stock data. The efficacy of the proposed model is compared with other benchmark models such as FPA-ELM, PSO-MLP, PSOElman, CSO-ARMA and GA-LSTM to prove its superiority. Results demonstrated that the GWO-ENN model provides accurate prediction for 1 day ahead prediction and outperforms the benchmark models taken for comparison.

关键词： Average relative variance bio-inspired algorithm Elman neural network Grey Wolf optimization Stock prediction

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A Chemical Reaction algorithm to Solve the Router Node Placement in Wireless Mesh Networks

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MOBILE NETWORKS & APPLICATIONS 2020年第5期25卷 1915-1928页

作者： Sayad, Lamri Bouallouche-Medjkoune, Louiza Aissani, Djamil Univ Bejaia Fac Exact Sci LAMOS Lab Bejaia 06000 Algeria Msila Univ Comp Sci Dept Msila 28000 Algeria

This paper considers the problem of router node placement (WMN-RNP) in wireless mesh networks (WMN). A wireless mesh network consists of three kinds of nodes: mesh clients, mesh routers and gateways interconnected via radio links. The problem considered in this paper is the following: given a set of mesh clients deployed in a rectangular area, determine the best placement of mesh routers so that both client coverage and network connectivity are maximized. This issue is known to be NP-hard since it can be modeled as a facility location problem. To solve this issue, we propose to use a metaheuristic technique inspired from the interactions between molecules in chemical reactions to reach a low stable energy state, namely Chemical Reaction Optimization algorithm (CRO). A simulation tool has been developed to compare the performance of our CRO algorithm to the existing Genetic algorithm (GA) and Simulated Annealing (SA). Simulation results show that our proposed algorithm can improve client coverage by 4.5% to 18% (3% to 17% respectively) and network connectivity by 5% to 61% (4.5% to 41% respectively) when compared to GA algorithm (SA algorithm respectively).

关键词： Wireless mesh network Router node placement bio-inspired algorithm Metaheuristic Chemical reaction optimization

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Artificial algae algorithm (AAA) for nonlinear global optimization

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APPLIED SOFT COMPUTING 2015年 31卷 153-171页

作者： Uymaz, Sait Ali Tezel, Gulay Yel, Esra Selcuk Univ Dept Comp Engn Konya Turkey Selcuk Univ Dept Environm Engn Konya Turkey

In this study, a novel bio-inspired metaheuristic optimization algorithm called artificial algae algorithm (AAA) inspired by the living behaviors of microalgae, photosynthetic species, is introduced. The algorithm is based on evolutionary process, adaptation process and the movement of microalgae. The performance of the algorithm has been verified on various benchmark functions and a real-world design optimization problem. The CEC'05 function set was employed as benchmark functions and the test results were compared with the algorithms of Artificial Bee Colony (ABC), Bee algorithm (BA), Differential Evolution (DE), Ant Colony Optimization for continuous domain (ACO(R)) and Harmony Search (HSpop). The pressure vessel design optimization problem, which is one of the widely used optimization problems, was used as a sample real-world design optimization problem to test the algorithm. In order to compare the results on the mentioned problem, the methods including ABC and Standard PSO (SPS02011) were used. Mean, best, standard deviation values and convergence curves were employed for the analyses of performance. Furthermore, mean square error (MSE), root mean square error (RMSE) and mean absolute percentage error (MAPE), which are computed as a result of using the errors of algorithms on functions, were used for the general performance comparison. AAA produced successful and balanced results over different dimensions of the benchmark functions. It is a consistent algorithm having balanced search qualifications. Because of the contribution of adaptation and evolutionary process, semi-random selection employed while choosing the source of light in order to avoid local minima, and balancing of helical movement methods each other. Moreover, in tested real-world application AAA produced consistent results and it is a stable algorithm. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Artificial algae algorithm bio-inspired algorithm Metaheuristic Optimization

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A novel metaheuristic inspired by horned lizard defense tactics

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ARTIFICIAL INTELLIGENCE REVIEW 2024年第3期57卷 1-65页

作者： Peraza-Vazquez, Hernan Pena-Delgado, Adrian Merino-Trevino, Marco Morales-Cepeda, Ana Beatriz Sinha, Neha Inst Politecn Nacl CICATA Altamira Km 14-5 Carretera Tampico Puerto Ind Altamira Mexico City 89120 Tamaulipas Mexico Univ Tecnol Altamira Puerto Ind Altamira Blvd Rios Km 3 100Puerto Ind Altamira Mexico City 89601 Tamaulipas Mexico TecNM Inst Tecnol Ciudad Madero Juventino Rosas & Jesus Urueta s-nCol Los Mangos Ciudad Madero 89318 Tamaulipas Mexico Indian Inst Informat Technol Bhagalpur Dept Comp Sci & Engn Bhagalpur 813210 Bihar India

This paper introduces HLOA, a novel metaheuristic optimization algorithm that mathematically mimics crypsis, skin darkening or lightening, blood-squirting, and move-to-escape defense methods. In crypsis behavior, the lizard changes its color by becoming translucent to avoid detection by its predators. The horned lizard can lighten or darken its skin, depending on whether or not it needs to decrease or increase its solar thermal gain. The skin darkening or lightening strategy is modeled by including the stimulating hormone melanophore rate( alpha\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}-MHS) that influences these skin color changes. Further, the move-to-evasion strategy is also mathematically described. The horned lizard's shooting blood defense mechanism, described as a projectile motion, is also modeled. These strategies balance exploitation and exploration mechanisms for local and global search over the solution space. HLOA performance is benchmarked with sixty-three optimization problems from the literature, testbench problems provided in IEEE CEC- 2017 "Constrained Real-Parameter Optimization", analyzed for dimensions 10, 30, 50, and 100, as well as testbench functions from IEEE CEC-06 2019 "100-Digit Challenge". Moreover, three real-world constraint optimization applications from IEEE CEC2020 and two engineering problems, the multiple gravity assist optimization and the optimal power flow problem, are also studied. Wilcoxon and Friedman statistics tests compare the HLOA algorithm results against ten recent bio-inspired algorithms. Wilcoxon shows that HLOA provides the optimal solution for most testbench functions more effectively than competing algorithms. At the same time, the Friedman statistics test ranks the HLOA first, and the n-dimensional analysis

关键词： Optimization bio-inspired algorithm Metaheuristics Horned Lizard Crypsis

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Continuous versions of firefly algorithm: a review

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ARTIFICIAL INTELLIGENCE REVIEW 2019年第3期51卷 445-492页

作者： Tilahun, Surafel Luleseged Ngnotchouye, Jean Medard T. Hamadneh, Nawaf N. Univ Zululand Dept Math Sci Private Bag X1001 ZA-3886 Kwa Dlangezwa South Africa Univ KwaZulu Natal Sch Math Stat & Comp Sci Private Bag X01 ZA-3209 Pietermaritzburg South Africa Saudi Elect Univ Dept Basic Sci Coll Sci & Theoret Studies Riyadh 11673 Saudi Arabia

Firefly algorithm is a swarm based metaheuristic algorithm designed for continuous optimization problems. It works by following better solutions and also with a random search mechanism. It has been successfully used in different problems arising in different disciplines and also modified for discrete problems. Unlike its easiness to understand and to implement;its effectiveness is highly affected by the parameter values. In addition modifying the search mechanism may give better performance. Hence different modified versions are introduced to overcome its limitations and increase its performance. In this paper, the modifications done on firefly algorithm for continuous optimization problems will be reviewed with a critical analysis. A detailed discussion on the modifications with possible future works will also be presented. In addition a comparative study will be conducted using forty benchmark problems with different dimensions based on ten base functions. The result shows that some of the modified versions produce superior results with a tradeoff of high computational time. Hence, this result will help practitioners to decide which modified version to apply based on the computational resource available and the sensitivity of the problem.

关键词： Firefly algorithm Optimization bio-inspired algorithm Swarm intelligence

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bio-inspired Approaches for Energy-Efficient Localization and Clustering in UAV Networks for Monitoring Wildfires in Remote Areas

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IEEE ACCESS 2021年 9卷 18649-18669页

作者： Arafat, Muhammad Yeasir Moh, Sangman Chosun Univ Dept Comp Engn Gwangju 61452 South Korea

In dynamic unmanned aerial vehicle (UAV) networks, localization and clustering are fundamental functions for cooperative control. In this article, we propose bio-inspired localization (BIL) and clustering (BIC) schemes in UAV networks for wildfire detection and monitoring. First, we develop a hybrid gray wolf optimization (HGWO) method and propose an energy-efficient three-dimensional BIL algorithm based on the HGWO, which reduces localization errors, avoids flip ambiguity in bounded distance measurement errors, and achieves high localization accuracy. In BIL, the bounding cube method is applied to reduce the initial search space. Second, we propose an energy-efficient BIC algorithm based on the HGWO. The BIC algorithm utilizes the gray wolf leadership hierarchy to improve clustering efficiency. We also develop an analytical model for determining the optimal number of clusters that provide the minimum number of transmissions. Finally, we propose a GWO-based compressive sensing (CS-GWO) algorithm to transmit data from cluster heads (CHs) to the base station (BS). The proposed CS-GWO constructs an efficient routing tree from CHs to the BS, thereby reducing the routing delay and the number of transmissions. Our performance evaluation shows that the proposed BIL and BIC significantly outperform conventional schemes in terms of various performance metrics under different scenarios.

关键词： Location awareness Clustering algorithms Global Positioning System Fires Energy efficiency Routing Monitoring bio-inspired algorithm cluster head clustering energy efficiency gray wolf optimization localization network lifetime routing protocol unmanned aerial vehicle network

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