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An Adaptive cuckoo algorithm-Based Optimization Method for Tourist Traffic Routes

JOURNAL OF CIRCUITS SYSTEMS AND COMPUTERS 2024年第2期33卷 2450033-2450033页

作者： Hong, Qian Sun, Yanbin Chongqing Inst Engn Sch Innovat & Entreprenurship Chongqing 400056 Peoples R China Chongqing Inst Engn Sch Elect Informat Chongqing 400056 Peoples R China

This paper presents in-depth research and analysis of the optimization method of tourist traffic routes using the adaptive cuckoo algorithm. The traditional cuckoo algorithm has the disadvantages of slow convergence speed and easy falling into local extremes. This paper proposes the iterative adaptive CHSACS algorithm based on iteration. Based on the original CS algorithm, a dynamic adaptive step control amount and a segmented weighted position update formula are introduced to give a class of improved CS algorithms to coordinate the problem of local search and global search of the CS algorithm and speed up the convergence speed at the later stage. To address the problem that the out-of-bounds nests interfere with the convergence of the algorithm, a memory strategy is introduced to relocate the out-of-bounds nests in the search space to improve the stability of the algorithm. Experiments are conducted on the iterative adaptive-based conductive CHSACS algorithm with test function sets. Compared with the original CS algorithm, and ACO algorithm, the CHSACS algorithm has faster convergence, higher search accuracy and a better ability to avoid local optima when dealing with continuous function optimization problems. For the dynamic travel path problem of travel time optimization, the road travel time and sightseeing time of different periods are predicted based on historical data, and the most traveled distance of travel time between two sights is dynamically searched by using the search mechanism based on time windows. The iterative adaptive CHSACS algorithm is used to continuously find the tourist traffic route with the shortest travel time travel path from all combinations of tour sequences. This paper combines the characteristics of bus vehicle scheduling itself, takes into account the interests of both bus companies and tourists, establishes a bus vehicle scheduling model with the departure interval as the independent variable, introduces this hybrid cuckoo algorith

关键词： Adaptive cuckoo algorithm tourist traffic route optimization

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Multi-objective optimization of integrated gas-electricity energy system based on improved multi-object cuckoo algorithm

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ENERGY SCIENCE & ENGINEERING 2021年第10期9卷 1839-1857页

作者： Yang, Bowen North China Elect Power Univ Sch Elect & Elect Engn Beijing 102206 Peoples R China

With the depletion of sources of primary energy across the world, the prospect of a global energy crisis in the near future has raised considerable concern. Among the many sources of clean energy, wind energy is considered to be the most effective means of sustainable and environmentally friendly energy. Traditional systems of energy based on oil and natural gas still operate in a discrete manner, and the promise offered by the mutual coordination between energy systems to draw complementary benefits has not yet been fully exploited. This has prompted all energy sectors to examine the optimal integration of interconnected gas-electricity energy systems. In this context, this study proposes a multi-objective collaborative operational optimization model of P2G for an interconnected, integrated gas-electricity energy system in case of uncertain wind power. Based on the effect of carbon capture by using P2G, the effect of coupling gas and electricity, and the cost of low-capacity reserves, the authors use conditional risk values to design four targets of optimization-the operating cost of the system, rate of consumption of the natural gas system, total carbon emissions and capacity stand-by cost-according to the power system, natural gas system, thermal energy system, and their elements of coupling from multiple perspectives to clarify the constraints on the model. Moreover, to reduce the likelihood of the optimal solution falling into the local optimum, the improved cuckoo algorithm is used to optimize the multi-objective scheduling model. The authors used a 10-node power system and a six-node natural gas system as examples for simulation to verify the proposed model. The results of empirical analysis show the following: (a) Once the P2G equipment had been connected, it reduced the total operating cost and improved the consumption of wind power of the natural gas system regardless of whether backup services were provided. (b) The P2G equipment could be combined with ga

关键词： conditional risk value cuckoo algorithm integrated gas-electricity energy system multi-objective optimal scheduling model

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Portfolio selection using hybrid algorithm of data envelopment analysis based on Kohonen neural network and cuckoo algorithm

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JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES 2016年第4期37卷 549-568页

作者： Razi, Farshad Faezy Shadloo, Naeimeh Islamic Azad Univ Dept Ind Management Semnan Iran

In stock markets, one of the main issues in which investors are involved is how to select stocks. If the investors select stocks rationally, he can achieve efficiencies greater than the market average. It is of great importance to find techniques that help investors in selecting stocks in particular at stock exchange. Investors are always looking for a set of stocks in the financial markets which is more profitable and less risky. The objective of this study is to presents a new model for portfolio selection. For portfolio selection, this model uses a hybrid algorithm of Data Envelopment Analysis (DEA) based Kohonen neural network and cuckoo search algorithm. The data are clustered using Kohonen neural network and then the efficiency is calculated separately for each cluster using DEA model based on the nature of the output. Pareto-optimal combination of risk and efficiency is analyzed via cuckoo algorithm.

关键词： Multiple-criteria decision-making Data envelopment analysis (DEA) Kohonen neural network cuckoo algorithm Portfolio optimization

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Optimal Overcurrent Relays Coordination in Microgrid Using cuckoo algorithm

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Energy Procedia 2016年 100卷 280-286页

作者： Amir Ahmarinejad Seyed Mohsen Hasanpour Mojtaba Babaei Mohammad Tabrizian Department of Electrical Engineering Yadegar-e-Imam Khomeini (RAH) Shahr-e-Rey Branch Islamic Azad University Tehran Iran

Microgrid(MG) is a controlled branch of Distributed Generations(DGs) and loads in distribution network and offers more potential in DGs operation and control. MG can operate with and without utility grid (Island mode). The operation of this system in islanded mode requires sophisticated control and protection schemes. MG protection should be capable of fault detection with appropriate sensitivity and selectivity in both rigid-connected and islanded modes. This paper presents solving the issue of short circuit level difference in both modes using fault current limiter (FCL). Overcurrent(O/C) relays coordination and fault current limiter impedance are selected in such a way that protection system has a suitable operation for both modes. For this purpose, artificial intelligence methods of Particle Swarm Optimization (PSO) algorithm and cuckoo algorithm have been used to achieve optimal relays coordination and fault current limiter impedance. The under study grid is a small MG related to Tehran Oil Refinery and the simulations have been running using MATLAB. The results indicate that fault current limiter can be used to solve the problem of short current level difference in both modes and cuckoo algorithm is a more effective method for optimization applications.

关键词： Microgrid Overcurrent Relays Fault Current Limiters cuckoo algorithm Particle Swarm Optimization algorithm

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An Optimized Electrifying Strategy for the Permanent-Magnet Spherical Actuator Based on the cuckoo algorithm

ELEKTROTEHNISKI VESTNIK

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ELEKTROTEHNISKI VESTNIK 2021年第1-2期88卷 8-16页

作者： Guo, Xiwen Wu, Siao Wang, Qunjing Li, Guoli Wen, Yan Anhui Univ Sch Elect Engn & Automat Hefei Anhui Peoples R China Anhui Univ Natl Engn Lab Energy Saving Motor & Control Tech Hefei Anhui Peoples R China Anhui Univ Ind Power Saving & Safety Lab Hefei Anhui Peoples R China Anhui Univ Collaborat Innovat Ctr Ind Energy Saving & Power Hefei Anhui Peoples R China

To assure a complex control of the permanent-magnet spherical actuator (PMSA), an optimized electrifying strategy based on the cuckoo algorithm is proposed. An electromagnetic analysis in mode and the PMSA torque values of the rotor and stator coils of different angles are obtained. A position-torque black-box model is set-up using the random forest method. Combined with the spin and tilt trajectory, the cuckoo algorithm optimizes the electrifying strategy resulting in a 5% similar to 18% power-loss reduction. Simulations and experiment show that by using the proposed strategy, the PMSA control system ensure a stable and effective performance of a reduced power-loss rate, thus providing a reference for the PMSM high-precision trajectory tracking control.

关键词： Permanent-Magnet Spherical Actuator (PMSA) Random Forest cuckoo algorithm Electrifying Strategy

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Short-term Wind power Prediction Model Based on cuckoo algorithm and BP neural Network

Short-term Wind power Prediction Model Based on Cuckoo Algor...

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IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Chengming Zhu Yunbing Wei Jianan Zhu Chengcheng Jiang School of Electric and Electronic Engineering Shanghai University of Engineering Science Shanghai China

ISBN: (数字)9781728167824

ISBN: (纸本)9781728167831

Aiming at the shortcomings of existing accurate prediction methods of wind power generation, a prediction model combining cuckoo algorithm and BP neural network is proposed. The optimal solution obtained by the cuckoo algorithm is used to assign the initial weight and threshold value of the BP neural network, and the historical power generation data is trained, and then the wind power is predicted by the BP neural network. In this paper, the actual wind power generation in Belgium is taken as an example to compare and analyze the error between BP neural network based on cuckoo algorithm and common BP neural network, BP neural network based on particle swarm optimization algorithm and BP neural network based on Genetic algorithm in wind power generation prediction. The experimental results show that the improved BP neural network model based on the cuckoo algorithm has better nonlinear fitting ability and higher accuracy than the other three network models for wind power prediction.

关键词： wind power generation cuckoo algorithm BP neural network prediction model BP neural networks Wind energy generation Cuculidae Wind energy Prediction models

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Collision avoidance decision-making by altering course based on Pareto cuckoo algorithm in multi-ship encounter

Collision avoidance decision-making by altering course based...

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作者： Xinlian XIE Aiyuan LIU Yukuan WANG Comprehensive Transportation Research Institute Dalian Maritime University

Aiming at collision avoidance decision-making problem in multi-ship encounter situation,the Pareto optimal theory is applied to the course altering decision making,a multiship encounter course alternation decision making model considering ship maneuverability is established,with collision avoidance effect,range loss,and rudder effect as three objective functions.A multi-objective cuckoo algorithm based on optimal solution set is designed to solve the optimal value of course angle *** the visibility clause and define the feasible area according to the navigation *** with the traditional weight-based multi-objective function solution,an example was verified in a certain area to verify the effectiveness of the model and *** results show that the Pareto cuckoo algorithm has a good effect in multi-ship encounter situations,and can provide the driver with various optimal collision avoidance plan.

关键词： Multi-ship encounter Course alteration Pareto cuckoo algorithm

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An approach for solving constrained reliability-redundancy allocation problems using cuckoo search algorithm

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Beni-Suef University Journal of Basic and Applied Sciences 2015年第1期4卷 14-25页

作者： Harish Garg Thapar University School of Mathematics and Computer Applications Patiala 147004 India

The main goal of the present paper is to present a penalty based cuckoo search (CS) algorithm to get the optimal solution of reliability – redundancy allocation problems (RRAP) with nonlinear resource constraints. The reliability – redundancy allocation problem involves the selection of components' reliability in each subsystem and the corresponding redundancy levels that produce maximum benefits subject to the system's cost, weight, volume and reliability constraints. Numerical results of five benchmark problems are reported and compared. It has been shown that the solutions by the proposed approach are all superior to the best solutions obtained by the typical approaches in the literature are shown to be statistically significant by means of unpaired pooled t-test.

关键词： Redundancy allocation cuckoo algorithm Reliability Optimization

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Optimal Design of a Brushless DC Motor, by cuckoo Optimization algorithm

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INTERNATIONAL JOURNAL OF ENGINEERING 2017年第5期30卷 668-677页

作者： Azari, M. Niaz Samami, M. Pahnehkolaei, S. M. Abedi Univ Sci & Technol Mazandaran Dept Elect Engn Behshahr Iran Islamic Azad Univ Dept Elect Engn Sari Branch Sari Iran

This contribution deals with an optimal design of a brushless DC motor, using optimization algorithms, based on collective intelligence. For this purpose, the case study motor is perfectly explained and its significant specifications are obtained as functions of the motor geometric parameters. In fact, the geometric parameters of the motor are considered as optimization variables. Then, the objective function has been defined. This function consists of three terms i.e. losses, construction cost and the volume of the motor which should be minimized simultaneously. Three algorithms i.e. cuckoo, genetic and particle swarm have been studied in this paper. It is noteworthy that, cuckoo optimization algorithm has been used for the first time for brushless DC motor design optimization. A comparative study between the mentioned optimization approaches shows that, cuckoo optimization algorithm has been converged to optimal response in less than 250 iterations and its standard deviation is. 0.03, while the convergence rate of the genetic and particle swarm algorithms are about 400 and 450 iterations with standard deviations of. 0.07 and. 0.06, respectively for the case study motor. The obtained results show the best performance for cuckoo optimization algorithm among all mentioned algorithms in brushless DC motor design optimization.

关键词： Brushless DC Motor cuckoo algorithm Objective Function Optimal Motor Design

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An Improved cuckoo Search algorithm for Target Assignment

An Improved Cuckoo Search Algorithm for Target Assignment

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2017 the 2nd International Conference on Mechatronics Engineering and Information Technology(ICMEIT 2017)

作者： Yunzhi Zhang Gang Wang Xiaoyang Gao Shengli Liu Tianxiang Gao School of Air and Missile Defense Air Force Engineering University

ISBN: (纸本)9781510840683

The purpose is to study an improved cuckoo search algorithms in target assignment via the analysis of the characteristics of air defense interception and the demand for weapon target assignment（WTA）.As for the threat of multi-batches and saturating targets,the random coding method based on the minimum position rule is used to initialize the *** addition,the convergence are ensured by adding adaptive communication and mutation *** improved algorithm is then simulated and analyzed by the air defense target interception case,and the results indicate that the improved algorithm is effectiveness and superiority in solving the problem of WTA,compared to the standard cuckoo and immune *** current study indicate that the improved algorithm provides a kind of effective method for solving the problem of WTA.

关键词： WTA cuckoo algorithm adaptive mutation operator adaptive communication operator

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