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Yildizdan G, Baş E. A Novel Binary Artificial Jellyfish Search Algorithm for Solving 0–1 Knapsack Problems. Neural Process Lett 2023. [DOI: 10.1007/s11063-023-11171-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/18/2023]
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Tefek MF. Rao algorithms based on elite local search method. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07932-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Li Z, Tang L, Liu J. A Memetic Algorithm Based on Probability Learning for Solving the Multidimensional Knapsack Problem. IEEE TRANSACTIONS ON CYBERNETICS 2022; 52:2284-2299. [PMID: 32673199 DOI: 10.1109/tcyb.2020.3002495] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The multidimensional knapsack problem (MKP) is a well-known combinatorial optimization problem with many real-life applications. In this article, a memetic algorithm based on probability learning (MA/PL) is proposed to solve MKP. The main highlights of this article are two-fold: 1) problem-dependent heuristics for MKP and 2) a novel framework of MA/PL. For the problem-dependent heuristics, we first propose two kinds of logarithmic utility functions (LUFs) based on the special structure of MKP, in which the profit value and weight vector of each item are considered simultaneously. Then, LUFs are applied to effectively guide the repair operator for infeasible solutions and the local search operator. For the framework of MA/PL, we propose two problem-dependent probability distributions to extract the special knowledge of MKP, that is, the marginal probability distribution (MPD) of each item and the joint probability distribution (JPD) of two conjoint items. Next, learning rules for MPD and JPD, which borrow ideas from competitive learning and binary Markov chain, are proposed. Thereafter, we generate MA/PL's offspring by integrating MPD and JPD, such that the univariate probability information of each item as well as the dependency of conjoint items can be sufficiently used. Results of experiments on 179 benchmark instances and a real-life case study demonstrate the effectiveness and practical values of the proposed MKP.
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Asha P, Natrayan L, Geetha BT, Beulah JR, Sumathy R, Varalakshmi G, Neelakandan S. IoT enabled environmental toxicology for air pollution monitoring using AI techniques. ENVIRONMENTAL RESEARCH 2022; 205:112574. [PMID: 34919959 DOI: 10.1016/j.envres.2021.112574] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 11/09/2021] [Accepted: 12/12/2021] [Indexed: 06/14/2023]
Abstract
In past decades, the industrial and technological developments have increased exponentially and accompanied by non-judicial and un-sustainable utilization of non-renewable resources. At the same time, the environmental branch of toxicology has gained significant attention in understanding the effect of toxic chemicals on human health. Environmental toxic agents cause several diseases, particularly high risk among children, pregnant women, geriatrics and clinical patients. Since air pollution affects human health and results in increased morbidity and mortality increased the toxicological studies focusing on industrial air pollution absorbed by the common people. Therefore, it is needed to design an automated Environmental Toxicology based Air Pollution Monitoring System. To resolve the limitations of traditional monitoring system and to reduce the overall cost, this paper designs an IoT enabled Environmental Toxicology for Air Pollution Monitoring using Artificial Intelligence technique (ETAPM-AIT) to improve human health. The proposed ETAPM-AIT model includes a set of IoT based sensor array to sense eight pollutants namely NH3, CO, NO2, CH4, CO2, PM2.5, temperature and humidity. The sensor array measures the pollutant level and transmits it to the cloud server via gateways for analytic process. The proposed model aims to report the status of air quality in real time by using cloud server and sends an alarm in the presence of hazardous pollutants level in the air. For the classification of air pollutants and determining air quality, Artificial Algae Algorithm (AAA) based Elman Neural Network (ENN) model is used as a classifier, which predicts the air quality in the forthcoming time stamps. The AAA is applied as a parameter tuning technique to optimally determine the parameter values of the ENN model. In-order to examine the air quality monitoring performance of the proposed ETAPM-AIT model, an extensive set of simulation analysis is performed and the results are inspected in 5, 15, 30 and 60 min of duration respectively. The experimental outcome highlights the optimal performance of the proposed ETAPM-AIT model over the recent techniques.
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Affiliation(s)
- P Asha
- Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, India
| | - L Natrayan
- Department of Mechanical Engineering, Saveetha School of Engineering, SIMATS, India
| | - B T Geetha
- Department of ECE, Saveetha School of Engineering, SIMATS, Saveetha University, India
| | - J Rene Beulah
- Department of CSE, College of Engineering and Technology, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, India
| | - R Sumathy
- Department of Computer Science and Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India
| | - G Varalakshmi
- Lecturer in Computer Science, Telangana Social Welfare Degree College for Womens, Siddipet, India
| | - S Neelakandan
- Department of CSE, R.M.K Engineering College, India.
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BinGSO: galactic swarm optimization powered by binary artificial algae algorithm for solving uncapacitated facility location problems. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07058-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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A simulated annealing based heuristic for a location-routing problem with two-dimensional loading constraints. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.108443] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Chowdary KM, Kuppili V. Enhanced Clustering and Intelligent Mobile Sink Path Construction for an Efficient Data Gathering in Wireless Sensor Networks. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-021-05415-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Tefek MF, Beşkirli M. JayaL: A Novel Jaya Algorithm Based on Elite Local Search for Optimization Problems. ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2021. [DOI: 10.1007/s13369-021-05677-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Hybrid evolutionary optimization for takeaway order selection and delivery path planning utilizing habit data. COMPLEX INTELL SYST 2021. [DOI: 10.1007/s40747-021-00410-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
AbstractThe last years have seen a rapid growth of the takeaway delivery market, which has provided a lot of jobs for deliverymen. However, increasing numbers of takeaway orders and the corresponding pickup and service points have made order selection and path planning a key challenging problem to deliverymen. In this paper, we present a problem integrating order selection and delivery path planning for deliverymen, the objective of which is to maximize the revenue per unit time subject to maximum delivery path length, overdue penalty, reward/penalty for large/small number of orders, and high customer scoring reward. Particularly, we consider uncertain order ready time and customer satisfaction level, which are estimated based on historical habit data of stores and customers using a machine-learning approach. To efficiently solve this problem, we propose a hybrid evolutionary algorithm, which adapts the water wave optimization (WWO) metaheuristic to evolve solutions to the main order selection problem and employs tabu search to route the delivery path for each order selection solution. Experimental results on test instances constructed based on real food delivery application data demonstrate the performance advantages of the proposed algorithm compared to a set of popular metaheuristic optimization algorithms.
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Chauhan S, Singh M, Aggarwal AK. Diversity driven multi-parent evolutionary algorithm with adaptive non-uniform mutation. J EXP THEOR ARTIF IN 2020. [DOI: 10.1080/0952813x.2020.1785020] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Sumika Chauhan
- Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, India
| | - Manmohan Singh
- Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, India
| | - Ashwani Kumar Aggarwal
- Department of Electrical and Instrumentation Engineering, Sant Longowal Institute of Engineering and Technology, Longowal, India
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A db-Scan Hybrid Algorithm: An Application to the Multidimensional Knapsack Problem. MATHEMATICS 2020. [DOI: 10.3390/math8040507] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
This article proposes a hybrid algorithm that makes use of the db-scan unsupervised learning technique to obtain binary versions of continuous swarm intelligence algorithms. These binary versions are then applied to large instances of the well-known multidimensional knapsack problem. The contribution of the db-scan operator to the binarization process is systematically studied. For this, two random operators are built that serve as a baseline for comparison. Once the contribution is established, the db-scan operator is compared with two other binarization methods that have satisfactorily solved the multidimensional knapsack problem. The first method uses the unsupervised learning technique k-means as a binarization method. The second makes use of transfer functions as a mechanism to generate binary versions. The results show that the hybrid algorithm using db-scan produces more consistent results compared to transfer function (TF) and random operators.
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Enhancing a machine learning binarization framework by perturbation operators: analysis on the multidimensional knapsack problem. INT J MACH LEARN CYB 2020. [DOI: 10.1007/s13042-020-01085-8] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Zheng YJ, Lu XQ, Du YC, Xue Y, Sheng WG. Water wave optimization for combinatorial optimization: Design strategies and applications. Appl Soft Comput 2019. [DOI: 10.1016/j.asoc.2019.105611] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Korkmaz S, Kiran MS. An artificial algae algorithm with stigmergic behavior for binary optimization. Appl Soft Comput 2018. [DOI: 10.1016/j.asoc.2018.01.001] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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A modified flower pollination algorithm for the multidimensional knapsack problem: human-centric decision making. Soft comput 2017. [DOI: 10.1007/s00500-017-2744-y] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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García J, Crawford B, Soto R, Castro C, Paredes F. A k-means binarization framework applied to multidimensional knapsack problem. APPL INTELL 2017. [DOI: 10.1007/s10489-017-0972-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
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Zhao C, Wu C, Chai J, Wang X, Yang X, Lee JM, Kim MJ. Decomposition-based multi-objective firefly algorithm for RFID network planning with uncertainty. Appl Soft Comput 2017. [DOI: 10.1016/j.asoc.2017.02.009] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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