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A new accurate and fast convergence cuckoo search algorithm for solving constrained engineering optimization problems

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INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS 2024年第3期18卷 2307-2337页

作者： Abdollahi, Mahdi Bouyer, Asgarali Arasteh, Bahman Victoria Univ Wellington Sch Engn & Comp Sci Wellington New Zealand Azarbaijan Shahid Madani Univ Fac Informat Technol & Comp Engn Tabriz Iran Istinye Univ Fac Engn & Nat Sci Dept Software Engn Istanbul Turkiye Khazar Univ Dept Comp Sci Baku Azerbaijan

In recent years, the cuckoo optimization algorithm (COA) has been widely used to solve various optimization problems due to its simplicity, efficacy, and capability to avoid getting trapped in local optima. However, COA has some limitations such as low convergence when it comes to solving constrained optimization problems with many constraints. This study proposes anew modified and adapted version of the cuckoo optimization algorithm, referred to as MCOA, that overcomes the challenge of solving constrained optimization problems. The proposed adapted version introduces a new coefficient that reduces the egg-laying radius, thereby enabling faster convergence to the optimal solution. Unlike previous methods, the new coefficient does not require any adjustment during the iterative process, as the radius automatically decreases along the iterations. To handle constraints, we employ the Penalty Method, which allows us to incorporate constraints into the optimization problem without altering its formulation. To evaluate the performance of the proposed MCOA, we conduct experiments on five well-known case studies. Experimental results demonstrate that MCOA outperforms COA and other state-of-the-art optimization algorithms in terms of both efficiency and robustness. Furthermore, MCOA can reliably find the global optimal solution for all the tested problems with in a reasonable iteration number.

关键词： Nonlinear optimization problem constrained problems engineering designing problems penalty function cuckoo optimization algorithm

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optimization of the reflux ratio of benzene-toluene stage distillation columns by the cuckoo algorithm

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Petroleum Science 2014年第3期11卷 446-453页

作者： Bahador Abolpour Ali Mohebbi Department of Chemical Engineering Faculty of Engineering Shahid Bahonar University of Kerman

In this study, an enthalpy-concentration method was applied in order to model a steady state continuous benzene-toluene mixture distillation column. For a distillation tower such as the benzene- toluene splitter, there are relatively few degrees of freedom that can be manipulated in order to minimize the total annualized cost. The reflux ratio can influence the steady-state operating point and therefore influence the total annualized cost. The trade-offs between reflux ratios and total annualized cost were discussed. The cuckoo optimization algorithm was applied to obtain a correlation for the optimum value of the reflux ratio as a power function of the economic parameters of energy price and capital cost. The results show that, at low energy price or high capital cost, the optimum reflux factor is high.

关键词： Benzene-toluene mixture distillation column cuckoo optimization algorithm optimized reflux ratio total annualized cost

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A short discussion about "Economic optimization design of shell-and-tube heat exchangers by a cuckoo-search-algorithm"

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APPLIED THERMAL ENGINEERING 2015年 76卷 535-537页

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

This short discussion concerns some inaccuracy in the recently published paper "M. Asadi et al. Economic optimization design of shell-and-tube heat exchangers by a cuckoo-search-algorithm. Applied thermal engineering, 2014". There was a mix-up between the cuckoo search (CS) with the cuckoo optimization algorithm (COA), which may cause further confusion if uncorrected. In this discussion, we intend to clarify the differences between both algorithms so as to provide a more accurate description of both algorithms. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： cuckoo search cuckoo optimization algorithm Swarm intelligence

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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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An efficient hybrid clustering method based on improved cuckoo optimization and modified particle swarm optimization algorithms

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APPLIED SOFT COMPUTING 2018年 67卷 172-182页

作者： Bouyer, Asgarali Hatamlou, Abdolreza Azarbaijan Shahid Madani Univ Fac Comp Engn & Informat Technol Tabriz Iran Islamic Azad Univ Dept Comp Sci Khoy Branch Khoy Iran

Partitional data clustering with K-means algorithm is the dividing of objects into smaller and disjoint groups that has the most similarity with objects in a group and most dissimilarity from the objects of other groups. Several techniques have been proposed to avoid the major limitations of K-Means such as sensitive to initialization and easily convergence to local optima. An alternative to solve the drawback of the sensitive to centroids' initialization in K-Means is the K-Harmonic Means (KHM) clustering algorithm. However, KHM is sensitive to the noise and easily runs into local optima. Over the past decade, many algorithms are developed for solving this problems based on evolutionary method. However, each algorithm has its own advantages, limitations and shortcomings. In this paper, we combined K-Harmonic Means (KHM) clustering algorithm with an improved cuckoo Search (ICS) and particle swarm optimization (PSO). ICS is intended to global optimum solution using Levy flight method through changing radius in a dynamic and shrewd manner. Therefore, it is faster than standard cuckoo search. ICS is effected with PSO to avoid falling into local optima. The proposed algorithm, called ICMPKHM, solves the local optima problem of KHM with significant improvement on efficacy and stability. Experiments with benchmark datasets show that the proposed algorithm is quite insensitive to the centroids' initialization. Comparative studies with other algorithms reveal that the proposed algorithm produce high quality and stable clustering results. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Data clustering Particle swarm optimization K-harmonic means cuckoo optimization algorithm

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A fuzzy approach for multi criteria decision making in diet plan ranking system using cuckoo optimization

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NEURAL COMPUTING & APPLICATIONS 2022年第16期34卷 13625-13638页

作者： Haseena, S. Saroja, S. Revathi, T. Mepco Schlenk Engn Coll Autonomous Dept Informat Technol Sivakasi Tamil Nadu India

In recent days, people have easily adapted to unhealthy diets due to their busy lifestyles. Inappropriate and unhealthy food intake leads to various health problems. As a result of poor health and lack of information about a healthy diet, people depend on medicines rather than concentrating on improving their food intake. Due to the wide range of dietary advice, it is difficult to choose the appropriate diet plan that satisfies their personalized nutritional needs. The proposed system ranks the diet plan by considering personal information like age, gender, height, weight, pressure and heart rate. Various diet plans like intermittent fasting, plant-based diets, low-carb diets, paleo diet, low-fat diets, Mediterranean diet, DASH diet, vegan diet, gluten-free diet, GM diet and egg diet are considered. Multi-criteria decision-making methods such as fuzzy AHP and fuzzy TOPSIS are also applied for the decision-making process to rank the best diet plan. Fuzzy AHP is used for weight generation, which is given to the cuckoo algorithm for optimization, and then, the fuzzy TOPSIS method helps in ranking the diet plan. Personal information is considered as criteria, and the diet plans are considered as alternatives for the decision-making process.

关键词： Decision-making Criteria Alternatives Fuzzy AHP cuckoo optimization algorithm Fuzzy TOPSIS

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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 (CICT)

作者： Desai, Vishnu Sai Mohanty, Ramakanta Keshav Mem Inst Technol Dept Comp Sci Engn Hyderabad Telangana India

ISBN: (纸本)9781538682159

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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Optimal Location of Unified Power Quality Conditioner in Distribution System for Power Quality Improvement

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年 83卷 309-324页

作者： Sarker, Jayanti Goswami, S. K. Techno India Dept Elect Engn Kolkata 700091 W Bengal India Jadavpur Univ Dept Elect Engn Kolkata 700032 W Bengal India

The increased use of nonlinear loads in distribution system is increasing the distortion in the voltage and current waveforms. Moreover, the distribution systems are inherently unbalanced. This paper presents cuckoo optimization algorithm (COA) based Unified Power Quality Conditioner (UPQC) allocation in three phase unbalanced distribution network. The performance of UPQC is studied in terms of minimization of load disturbance during fault condition in the test systems, % reduction of total harmonic distortion and individual harmonics, minimization of real power loss, decrease in voltage unbalance and increase in cost savings during normal operating condition. The performance of COA is compared with other soft computing techniques to get the better results, i.e., better voltage profile, the optimal location and optimal number of UPQCs. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Unified Power Quality Conditioner (UPQC) cuckoo optimization algorithm Optimal placement problem Total harmonic distortion minimization Real power loss minimization Load disturbance minimization during fault

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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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Estimation of the CO₂-oil minimum miscibility pressure for enhanced oil recovery

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PETROLEUM SCIENCE AND TECHNOLOGY 2016年第22期34卷 1847-1854页

作者： Choubineh, Abouzar Mousavi, Seyede Robab Ayouri, Masih Vafaee Ahmadinia, Masoud Choubineh, Dariush Baghban, Alireza Petr Univ Technol Petr Dept Ahvaz Iran Shiraz Univ Petr Dept Shiraz Iran Petr Univ Technol Automat & Instrumentat Dept Ahvaz Iran Polytech Univ Turin Petr Dept Turin Italy Islamic Azad Univ Petr Dept Gachsaran Iran Islamic Azad Univ Marvdasht Branch Young Researcher & Elite Club Marvdasht Iran

The authors' aim was to present new models based on artificial neural network (ANN) and two optimization algorithms including cuckoo optimization algorithm (COA) and teaching learning based optimization (TLBO) to predict the pure and impure CO2 MMP. Thirty-four and 11 training and testing data sets were used to develop these models with following inputs: reservoir temperature, the mole percent of volatile oil components (C-1 and N-2), mole percent of intermediate oil components (C-2-C-4, CO2, and H2S), molecular weight of C5+ fraction in oil phase (MWC5+) and mole percentage of CO2, N-2, C-1, C-4, and H2S in the injected gas. Statistical comparisons show that although two models yield acceptable results, the ANN-TLBO model has better performance with the lower mean absolute percentage error (2.6%) and standard deviation (3.37%) and the higher coefficient of determination (0.993). Moreover, among the available correlations, the Cronquist's (1978;corrected by Sebastian etal., 1985) correlations have better performance. Finally, the sensitivity analysis on the ANN-TLBO showed that MWC5+ and reservoir temperature are the most influential parameters in determining the CO2 MMP, respectively.

关键词： Artificial neural network cuckoo optimization algorithm minimum miscibility pressure sensitivity analysis teaching learning-based optimization

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