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Development of a two-stage gene selection method that incorporates a novel hybrid approach using the cuckcio optimization algorithm and harmony search for cancer classification

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JOURNAL OF BIOMEDICAL INFORMATICS 2017年 67卷 11-20页

作者： Elyasigomari, V. Lee, D. A. Screen, H. R. C. Shaheed, M. H. Queen Mary Univ London Sch Engn & Mat Sci London E1 4NS England

For each cancer type, only a few genes are informative. Due to the so-called 'curse of dimensionality' problem, the gene selection task remains a challenge. To overcome this problem, we propose a two stage gene selection method called MRMR-COA-HS. In the first stage, the minimum redundancy and maximum relevance (MRMR) feature selection is used to select a subset of relevant genes. The selected genes are then fed into a wrapper setup that combines a new algorithm, COA-HS, using the support vector machine as a classifier. The method was applied to four microarray datasets, and the performance was assessed by the leave one out cross-validation method. Comparative performance assessment of the proposed method with other evolutionary algorithms suggested that the proposed algorithm significantly outperforms other methods in selecting a fewer number of genes while maintaining the highest classification accuracy. The functions of the selected genes were further investigated, and it was confirmed that the selected genes are biologically relevant to each cancer type. (C) 2017 Published by Elsevier Inc.

关键词： Gene selection Minimum redundancy and maximum relevance (MRMR) Evolutionary algorithms cuckoo optimization algorithm Harmony search algorithm COA-HS

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A multi-objectives scheduling algorithm based on cuckoo optimization for task allocation problem at compile time in heterogeneous systems

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EXPERT SYSTEMS WITH APPLICATIONS 2016年 60卷 234-248页

作者： Akbari, Mehdi Rashidi, Hassan Islamic Azad Univ Fac Comp & Informat Technol Engn Qazvin Branch Qazvin Iran Allameh Tabatabai Univ Dept Math & Comp Sci Tehran Iran

To handle scheduling of tasks on heterogeneous systems, an algorithm is proposed to reduce execution time while allowing for maximum parallelization. The algorithm is based on multi-objective scheduling cuckoo optimization algorithm (MOSCOA). In this algorithm, each cuckoo represents a scheduling solution in which the ordering of tasks and processors allocated to them are considered. In addition, the operators of cuckoo optimization algorithm means laying and immigration are defined so that it is usable for scheduling scenario of the directed acyclic graph of the problem. This algorithm adapts cuckoo optimization algorithm operators to create proper scheduling in each stage. This ensures avoiding local optima while allowing for global search within the problem space for accelerating the finding of a global optimum and delivering a relatively optimized scheduling with the least number of repetitions. Moving toward global optima is done through a target immigration operator in this algorithm and schedules in each repetition are pushed toward optimized schedules to secure global optima. The results of MOSCOA implementation on a large number of random graphs and real-world application graphs with a wide range characteristics show MOSCOA superiority over the previous task scheduling algorithms. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Task scheduling Heterogeneous systems cuckoo optimization algorithm Meta-heuristic algorithm Makespan

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optimization of peroxone reaction rate using metaheuristic approach in the dearomatization and discoloration process

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ENVIRONMENTAL PROGRESS & SUSTAINABLE ENERGY 2018年第2期37卷 695-702页

作者： Kordkandi, Salman Alizadeh Motlagh, Amir Mohaghegh Sahand Univ Technol Dept Chem Engn Tabriz 513351996 Iran Sahand Univ Technol Dept Chem Engn EERC Tabriz 513351996 Iran Univ Cent Florida Dept Civil Environm & Construct Engn Orlando FL 32816 USA Calif State Univ Sacramento Dept Civil Engn Sacramento CA 95819 USA

The main objective of this research focused on the optimization of apparent reaction rate constants (k(app)) for degradation of a diazo compound, reactive yellow 42 (RY42) using peroxone reaction. Artificial Neural Network (ANN) was adopted to evaluate the effects of initial RY42 concentration, influent ozone mass flow rate, initial ozone (O-3)/peroxide hydrogen ratio (H2O2), and pH on the discoloration rate constant. A proposed model by ANN was chosen after analysis of the topology based on R-square (R-2) and mean squared error (MSE) criteria followed by coupling the obtained best net with a novel evolutionary algorithm named cuckoo optimization algorithm (COA). Although the relative importance of pH and O-3/H2O2 on k(app) was at the same level (about 18%), initial dye concentration (34%), and influent ozone mass flow rate (30%) were the most important factors. A systematic ANN-COA approach indicated that the optimum values of pH and O-3/H2O2 for 150 mgL(-1) of RY42 were 7.5 and 1.44 Lh(-1), respectively, resulted in relatively different k(app) for decolorization (0.18 min(-1)) and dearomatization (0.056 min(-1)). To confirm the validity of kinetic analysis, the predicted values were correlated with experimental values, which indicated the high degree of accuracy (R-2= 0.98) for optimization of the peroxone process. This research with the experimental and modeling studies can be used as a guide for the design of full-scale treatment processes for various types of industrial wastewater containing aromatic compounds with diazo groups. (c) 2017 American Institute of Chemical Engineers Environ Prog, 37: 695-702, 2018

关键词： peroxone processes diazo dye kinetic analysis artificial neural network cuckoo optimization algorithm

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A comprehensive study of cuckoo-inspired algorithms

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NEURAL COMPUTING & APPLICATIONS 2018年第2期29卷 345-361页

作者： Abdel-Basset, Mohamed Hessin, Abdel-Naser Abdel-Fatah, Lila Zagazig Univ Fac Comp & Informat Zagazig Egypt

Nature-inspired metaheuristic algorithms are considered as the most effective techniques for solving various optimization problems. This paper provides a briefly review of the key features of the cuckoo-inspired metaheuristics: cuckoo search (CS) and cuckoo optimization algorithm (COA). In addition, it discusses some of their important and emerging studies, investigates their applications in several fields, and finally clarifies the differences between both algorithms so as to remove confusion between them.

关键词： Metaheuristic cuckoo search cuckoo optimization algorithm

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Bayesian network structure learning based on cuckoo search algorithm 6

Bayesian network structure learning based on cuckoo search a...

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6th Iranian Joint Congress on Fuzzy and Intelligent Systems (CFIS)

作者： Askari, Mahbobe Bani Asad Ahsaee, Mostafa Ghazizadeh Besat Univ Dept Comp Engn Kerman Iran Shahid Bahonar Univ Dept Comp Engn Kerman Iran

ISBN: (纸本)9781538628362

Bayesian network is a graphical model based on probabilities to represent and inference in uncertain conditions. In the field of Bayesian network, structure learning from data is an important challenge. One of the methods to learn structure of a Bayesian Networks is score and search method. In this paper, a new method for learning the structure of the Bayesian network is presented by using cuckoo optimization algorithm. In accordance with the cuckoo search algorithm, in the proposed algorithm, a population of directed acyclic graphs is created, which are equivalent to the cuckoos. Each graph has a score which indicates its fitness. The algorithm is repeated until it finds the best solution or an acceptable network structure. In each iteration, it searches for the directed acyclic graph with the best score. Based on the practical results obtained, the proposed algorithm has a better performance than the other algorithms and offers higher scores.

关键词： Bayesian network structure learning cuckoo optimization algorithm

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Multi-objective simulation optimization for selection and determination of order quantity in supplier selection problem under uncertainty and quality criteria

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2017年第1-4期93卷 161-173页

作者： Shadkam, Elham Bijari, Mehdi Isfahan Univ Technol Dept Ind & Syst Engn Esfahan Iran

Simulation optimization is providing solutions to practical stochastic problems. Supplier selection is one of the most important decisions that determine the survival of an organization. In this paper, a novel multi-objective simulation optimization method to make decisions on selecting the suppliers and determining the order quantities is proposed. Regarding the fact that a real supply chain is multi-objective with uncertain parameters and includes both quantitative and qualitative variables, the proposed method considers these points and is applicable to real-world problems. This method also considers supplier selection and order quantity allocation to each supplier, which are totally related, as an integrated model. The proposed method consists of four basic modules: cuckoo optimization algorithm (COA), Discrete Event Simulation (DES), Supply Chain Model (SCM), and Generalized Data Envelopment Analysis (GDEA). Unlike many multi-objective methods, the proposed method is not limited to the number of objective functions and this is one of its main benefits. It also pays attention to the efficiency of the organization and, at the same time, finding inputs which result in best output amounts. This method, in addition to the convergence criterion, pays special attention to the dispersion of the Pareto frontier as the second criterion for choosing the good solutions. For implementation of the proposed method, the numerical results for the problem of supplier selection in multi-product, multi-customer modes, and uncertain and qualitative variables are discussed and the Pareto frontiers are presented. The proposed method in this paper is compared with a similar method, and the results show the efficiency of the proposed method.

关键词： Supplier selection problem Simulation optimization Multi-objective problem cuckoo optimization algorithm Generalized Data Envelopment Analysis Efficiency

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Prediction and minimization of blast-induced ground vibration using two robust meta-heuristic algorithms

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ENGINEERING WITH COMPUTERS 2017年第4期33卷 835-851页

作者： Faradonbeh, Roohollah Shirani Monjezi, Masoud Tarbiat Modares Univ Fac Engn Tehran Iran

During blasting operations in open pit mines, most of the blast energy (approximately 40%) is wasted due to ground vibration. This phenomenon causes damages to the surrounding structures and pit slope. Therefore, prediction of ground vibration with an appropriate degree of accuracy is important to identify safety area of blasting. In this paper, in the first step, a predictive equation based on gene expression programming (GEP) was developed to estimate ground vibrations of blasting operations conducted in Gol-E-Gohar iron mine. For this aim, 115 blasting operations were identified and the most effective parameters on peak particle velocity (PPV), i.e., burden, spacing, stemming, hole-depth, hole-diameter, powder factor, maximum charge per delay and distance from the blast face were collected from the mine. Capability of the developed GEP model was compared with a non-linear multiple regression (NLMR) model and five conventional equations. The obtained results revealed that the developed GEP model is more efficient compared to the other models in predicting PPV. In the second step, to optimize the GEP predictive model, cuckoo optimization algorithm (COA) was employed and proposed. To do this, several strategies were defined and then several optimized blasting patterns were determined for each strategy. It was found that by developing the COA model, a significant reduction can be happened in PPV values.

关键词： Blasting Ground vibration Gene expression programming Non-linear multiple regression Conventional equations cuckoo optimization algorithm

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ANN-cuckoo optimization Technique to Predict Software Cost Estimation

ANN-Cuckoo Optimization Technique to Predict Software Cost E...

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Conference on Information and Communication Technology

作者： Vishnu Sai Desai Ramakanta Mohanty Dept. of Computer Science Engineering Keshav Memorial Institute of Technology

ISBN: (纸本)9781538682166

Software cost is of the most complex and vital aspect in consideration when software is in its development stages. To determine the amount of time, effort and resources required to complete the project successfully translate to Software Cost Estimation (SCE). Thus far, many models have been suggested such as Fuzzy Logic, Neural Networks, Support Vector Machines, Ant Colony optimization, Genetic algorithms, Decision Trees, Case-Based Reasoning and Soft Computing Techniques. Such computational models have contributed to a large extent in this arena. Yet, there still lies immense scope to apply optimization methods. Neural Networks are the most utilized techniques in software cost estimation by researchers. In this paper, we propose the use of a new model, i.e. Artificial Neural Networks (ANN) trained using cuckoo optimization algorithm (COA) to predict Software Cost Estimation. The key goal is to exhibit use of a novel learning procedure for ANN to better predict SCE. The proposed model is verified with the ISBSG dataset and results are compared with existing models. The results shown are in terms of Root Mean Squared Error (RMSE) and Mean Magnitude of Relative Error (MMRE).

关键词： Software cost estimation Neural networks cuckoo optimization algorithm RMSE MMRE

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Discussion of "Estimation of Reference Evapotranspiration Using Neural Networks and cuckoo Search algorithm" by Shahaboddin Shamshirband, Mohsen Amirmojahedi, Milan Goci, Shatirah Akib, Dalibor Petkovi, Jamshid Piri, and Slavisa Trajkovic

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JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING 2018年第2期144卷 07017023-07017023页

作者： Fister, Iztok, Jr. Fister, Iztok Yang, Xin-She Univ Maribor Fac Elect Engn & Comp Sci SLO-2000 Maribor Slovenia Middlesex Univ London Sch Sci & Technol London NW4 4BT England

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Forecasting Monthly Electricity Demands by Wavelet Neuro-Fuzzy System Optimized by Heuristic algorithms

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INFORMATION 2018年第3期9卷 51-51页

作者： Chen, Jeng-Fung Quang Hung Do Thi Van Anh Nguyen Thi Thanh Hang Doan Feng Chia Univ Dept Ind Engn & Syst Management Taichung 40724 Taiwan Univ Transport Technol Fac Informat Technol Hanoi 100000 Vietnam

Electricity load forecasting plays a paramount role in capacity planning, scheduling, and the operation of power systems. Reliable and accurate planning and prediction of electricity load are therefore vital. In this study, a novel approach for forecasting monthly electricity demands by wavelet transform and a neuro-fuzzy system is proposed. Firstly, the most appropriate inputs are selected and a dataset is constructed. Then, Haar wavelet transform is utilized to decompose the load data and eliminate noise. In the model, a hierarchical adaptive neuro-fuzzy inference system (HANFIS) is suggested to solve the curse-of-dimensionality problem. Several heuristic algorithms including Gravitational Search algorithm (GSA), cuckoo optimization algorithm (COA), and cuckoo Search (CS) are utilized to optimize the clustering parameters which help form the rule base, and adaptive neuro-fuzzy inference system (ANFIS) optimize the parameters in the antecedent and consequent parts of each sub-model. The proposed approach was applied to forecast the electricity load of Hanoi, Vietnam. The constructed models have shown high forecasting performances based on the performance indices calculated. The results demonstrate the validity of the approach. The obtained results were also compared with those of several other well-known methods including autoregressive integrated moving average (ARIA) and multiple linear regression (MLR). In our study, the wavelet CS-HANFIS model outperformed the others and provided more accurate forecasting.

关键词： wavelet transform neuro-fuzzy system cuckoo Search cuckoo optimization algorithm Gravitational Search algorithm neural networks forecasting monthly electricity demand Vietnam

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