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A discrete binary version of bat algorithm for multidimensional knapsack problem

INTERNATIONAL JOURNAL OF bio-inspired COMPUTATION 2014年第2期6卷 140-152页

作者： Sabba, Sara Chikhi, Salim Constantine Univ 2 Fac Engn Sci Dept Comp Sci Constantine 25017 Algeria

The nature has become a main inspiration source of scientists for developing new intelligent systems and techniques. Nature-inspired meta-heuristics is a kind of algorithms that imitate the social behaviour of some biological species. The bat algorithm (BA) is a new bio-inspired algorithm recently introduced by Yang (2010a). It is an optimisation method that is based on the echolocation behaviour of microbats. Firstly, the BA has been proposed for continuous problems. In this paper, we propose a discrete binary bat algorithm (BinBA) for solving the optimisation problems in binary space. The proposed algorithm is based on the sigmoid function used by Kennedy and Eberhart in 1997 for their binary particle swarm optimisation algorithm. The BinBA was tested on hard instances of the multidimensional knapsack problem. The obtained results are very promising compared to other bio-inspired algorithms.

关键词： discrete optimisation problem bio-inspired algorithm bat algorithm binary bat algorithm multidimensional knapsack problem MKP

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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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Phototropic algorithm for global optimisation problems

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APPLIED INTELLIGENCE 2021年第8期51卷 5965-5977页

作者： Chandra S. S., Vinod Hareendran S., Anand Univ Kerala Dept Comp Sci Thiruvananthapuram Kerala India Muthoot Inst Technol & Sci Dept Comp Sci & Engn Kochi India

Problem solving and decision-making have a vital role to play in both technical and non-technical fields. Some decisions are simple while others require more effort and time to solve. This article introduces a new problem solving technique called Phototropic optimization algorithm, inspired from the optimised growth pattern in plants. It has been observed that the stem tips of a plant always grow towards sunlight. In this algorithm, the underlying hormonal mechanism of phototropism is emulated to solve computational problems. This phenomenon has indicated strong prospects of algorithmic efficiency and invites further research into prospective computational applications. Phototropic algorithm is developed as an optimization technique to solve real time application such as shortest path finding problems, travelling salesman problem, finding congestion in a network or any similar problem seen around. A prototype on finding the minimal distance between any two nodes in the physical network is modelled here. The asymptotic time complexity analysis shows the algorithm routes packages in O (n log n). Comparison with the traditional algorithms gives sufficient evidence for the efficiency of this proposal. This can be implemented over Software Defined Networks (SDN) for increasing system capabilities in route analytics and functionalities. Extension of this optimization algorithm is useful to solve various real time problems.

关键词： bio-inspired algorithm Congestion based routing Optimising model Phototropic algorithm Shortest path

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

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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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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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FPGA-Based Hardware/Software Co-Design of a bio-inspired SAT Solver

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IEEE ACCESS 2020年 8卷 49053-49065页

作者： Nguyen, Anh Hoang Ngoc Aono, Masashi Hara-Azumi, Yuko Tokyo Inst Technol Dept Informat & Commun Tokyo 1528550 Japan Keio Univ Fac Environm & Informat Studies Tokyo 1088345 Japan Amoeba Energy Co Ltd Yokohama Kanagawa Japan

For various kinds of Internet of Things (IoT) systems whose control rules can be expressed in a Satisfiability (SAT) problem, this work aims at realizing an IoT-oriented FPGA-based SAT solver leveraging a bio-inspired algorithm, AmoebaSAT, using a hardware/software co-design approach. With regard to the software component, we extended the baseline algorithm to escape from local minima more quickly and achieve significant reduction in iteration count. With regard to hardware, we fully extracted the fine-grained parallelism of the algorithm to further accelerate the solution search. Through our evaluations using several benchmarks of varying variable count and complexity, we demonstrated the efficiency of our solver, especially for larger practical SAT instances. Compared with three state-of-the-art solvers (i.e., one software implementation of the original AmoebaSAT algorithm and two FPGA-based hardware solvers), we achieved an average of 15.9 x and up to 48 x reduction in iteration count. Furthermore, through in-depth analyses of the experimental results, we provided the essential findings on the relationship between the problem's complexity and the SAT algorithm that can be leveraged for extensions of both the hardware and software designs.

关键词： Hardware Stochastic processes Software algorithms Parallel processing Internet of Things Software Search problems bio-inspired algorithm SAT solver FPGA high-level synthesis

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Seasons optimization algorithm

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ENGINEERING WITH COMPUTERS 2022年第2期38卷 1845-1865页

作者： Emami, Hojjat Univ Bonab Bonab Iran

This paper introduces a new stochastic bio-inspired optimization algorithm, denoted as seasons optimization (SO) algorithm. This algorithm is inspired by the growth cycle of trees in different seasons of a year. It is an iterative and population-based algorithm working with a population of initial solutions known as aforest. Each individual in the forest is referred to as atree. Until the termination conditions are satisfied, the trees in the forest are updated to a new generation by applying four operators similar to the trees' life cycles in nature:renew,competition,seeding, andresistance. These operators hopefully cause the trees to converge towards the global optimum of the optimization problem. The effectiveness of the proposed SO algorithm is evaluated using multi-variable single-objective test problems and compared with several well-known baseline and state-of-the-art algorithms. The results show that the proposed algorithm outperformed its counterparts in terms of solution quality and finding the global optimum on most benchmark functions.

关键词： Trees' growth cycle Optimization Meta-heuristic bio-inspired algorithm Seasons optimization (SO)

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