Conventional Cuckoo Search Algorithm Research Articles

Underdetermined direction of arrival estimation for multiple input and multiple outputs sparse channel based on Bayesian learning framework

Direction of arrival (DOA) estimation for a sparse channel has attracted serious attention recently. Better signal analysis and denoising achieve accuracy in DOA determination. This paper proposes an underdetermined DOA estimation for multiple input and multiple outputs (MIMO) sparse channels. A novel multi-kernel-based non-negative sparse Bayesian learning (MK NNSBL) framework is implemented using the multiplied form of basis vector within the manifold matrix for a defined grid. Meanwhile, virtual antenna locations are reconfigured by exploiting the conventional cuckoo search algorithm (CCSA) for the fine reception of incoming signals on a nonuniform linear array (NULA). The simulated results reveal that the novel approach outperforms in its optimal root mean square error (RMSE) for various signal-to-noise ratio (SNR) limits and the compilation time. The convergence comparative graph indicates the improved performance in the proposed framework over existing algorithms.

ECS-NL: An Enhanced Cuckoo Search Algorithm for Node Localisation in Wireless Sensor Networks.

Node localisation plays a critical role in setting up Wireless Sensor Networks (WSNs). A sensor in WSNs senses, processes and transmits the sensed information simultaneously. Along with the sensed information, it is crucial to have the positional information associated with the information source. A promising method to localise these randomly deployed sensors is to use bio-inspired meta-heuristic algorithms. In this way, a node localisation problem is converted to an optimisation problem. Afterwards, the optimisation problem is solved for an optimal solution by minimising the errors. Various bio-inspired algorithms, including the conventional Cuckoo Search (CS) and modified CS algorithm, have already been explored. However, these algorithms demand a predetermined number of iterations to reach the optimal solution, even when not required. In this way, they unnecessarily exploit the limited resources of the sensors resulting in a slow search process. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to minimise the Average Localisation Error (ALE) and the time taken to localise an unknown node. In this algorithm, we have implemented an Early Stopping (ES) mechanism, which improves the search process significantly by exiting the search loop whenever the optimal solution is reached. Further, we have evaluated the ECS algorithm and compared it with the modified CS algorithm. While doing so, note that the proposed algorithm localised all the localisable nodes in the network with an ALE of 0.5–0.8 m. In addition, the proposed algorithm also shows an decrease in the average time taken to localise all the localisable nodes. Consequently, the performance of the proposed ECS algorithm makes it desirable to implement in practical scenarios for node localisation.

Optimized cuckoo search algorithm using tournament selection function for robot path planning

Acceptability of mobile robots in various applications has led to an increase in mobile robots’ research areas. Path planning is one of the core areas which needs to be improvised at a higher level. Optimization is playing a more prominent role these days. The nature-inspired algorithm is contributing to a greater extent in achieving optimization. This article presents the modified cuckoo search algorithm using tournament selection function for robot path planning. Path length and Path time are the algorithm’s parameters to validate the effectiveness and acceptability of the output. The cuckoo search algorithm’s fundamental working principle is taken as the baseline, and the tournament selection function is adapted to calculate the optimum path for robots while navigating from its initial position to final position. The tournament selection function is replacing the concept of random selection done by the cuckoo search algorithm. The use of tournament selection overcomes local minima for robots while traversing in the configuration space and increases the probability of giving more optimum results. The conventional cuckoo search algorithm whose random selection mechanism may lead to premature convergence may fall into the local minima. The use of tournament selection function increases the probability of giving better results as it allows all the possible solution to take part in the tournament. The results are analysed and compared with other relevant work like cuckoo search algorithm and particle swarm optimization technique and presented in the article. The proposed method produced a better output in terms of path length and path time.

An improved cuckoo search with reverse learning and invasive weed operators for suppressing sidelobe level of antenna arrays

AbstractIn order to overcome some shortcomings including the premature convergence and slow convergence speed at later evolution stage of conventional cuckoo search (CS) algorithm for the sidelobe suppressions of antenna arrays, an improved CS with reverse learning and invasive weed operators (ICSRLIWO) is proposed. First, ICSRLIWO algorithm generates reverse populations through opposition‐based learning method, then selects better individuals in the mixed population consists of the original and reversed population to form a high‐quality initial population. Second, ICSRLIWO introduces the reproducing, space diffusion and survival competition operators of invasive weed optimization to improve the population diversity and global search ability of conventional CS algorithm. Simulations are conducted based on 30 test functions in the CEC 2014 test suit to verify the performance of the proposed approach, and the results show that ICSRLIWO has higher solution accuracy and faster convergence speed than other comparison algorithms. In addition, ICSRLIWO also has advantages in solving the array antenna beam pattern optimization problems.

A Physical Layer Security Approach Based on Optical Beamforming for Indoor Visible Light Communication

Secure transmissions are important for visible light communication (VLC) systems. However, conventional information security technologies may be not suitable for VLCs due to the limited hardware resources especially for the receivers. In this letter, we conduct a security zone in the room and propose a physical layer security approach based on optical beamforming to achieve secure transmissions. First, we formulate an optimization problem to maximize the received signal power of the security zone so that the eavesdroppers in the insecurity zone can not obtain enough signal power for decoding. Then, a cuckoo search with adaptive searching and population mutation (CSASPM) algorithm is proposed to solve the formulated optimization problem. CSASPM introduces the adaptive searching and population mutation factors to improve the performance of conventional cuckoo search algorithm so that making it more suitable for the secure transmission optimization of VLC. Simulation results demonstrate that the proposed approach is effective and has better performance than other comparison methods.

An improved Cuckoo search localization algorithm for UWB sensor networks

Conventional Cuckoo search (CS) localization method can obtain good positioning results that are highly accurate and robust. However, its positioning performance is constrained by the limited distance information available between the unknown node and the anchor node. In order to further enhance the positioning accuracy of the CS method, an improved CS localization algorithm is proposed that can take advantage of all of the distance information available. In the positioning process, an objective function which contains the distance information among the unknown nodes is given. Firstly, we use this distance information between the anchor node and the unknown node to determine an initial position through the conventional CS method. Then, based on the initial position, all of the distance information available is used to compute a more precise position. The simulation results demonstrate that the proposed algorithm can enhance the positioning accuracy in comparison with the conventional CS algorithm.

Application of a modified VES production function model

<p style='text-indent:20px;'>In the analyses on economic growth factors, researchers generally use the production function model to calculate the contribution rates of influencing factors to economic growth. The paper proposes a new modified VES production function model. As for the model's parameter estimation, the conventional optimization methods are complicated, generally require information like the gradient of objective function, and have the poor convergence rate and precision. The paper gives a modern intelligent algorithm, i.e., the cuckoo search algorithm, which has the strong robustness, can be realized easily, has the fast convergence rate and can be used flexibly. To enhance the convergence rate and precision, the paper improves the conventional cuckoo search algorithm. Using the new model, the paper gives a method calculating the contribution rates of economic growth influencing factors scientifically. Finally, the paper calculates the contribution rates of influencing factors to economic growth in Shanghai City, China.

Optimal Scheduling of Large-Scale Wind-Hydro-Thermal Systems with Fixed-Head Short-Term Model

In this paper, a Modified Adaptive Selection Cuckoo Search Algorithm (MASCSA) is proposed for solving the Optimal Scheduling of Wind-Hydro-Thermal (OSWHT) systems problem. The main objective of the problem is to minimize the total fuel cost for generating the electricity of thermal power plants, where energy from hydropower plants and wind turbines is exploited absolutely. The fixed-head short-term model is taken into account, by supposing that the water head is constant during the operation time, while reservoir volume and water balance are constrained over the scheduled time period. The proposed MASCSA is compared to other implemented cuckoo search algorithms, such as the conventional Cuckoo Search Algorithm (CSA) and Snap-Drift Cuckoo Search Algorithm (SDCSA). Two large systems are used as study cases to test the real improvement of the proposed MASCSA over CSA and SDCSA. Among the two test systems, the wind-hydro-thermal system is a more complicated one, with two wind farms and four thermal power plants considering valve effects, and four hydropower plants scheduled in twenty-four one-hour intervals. The proposed MASCSA is more effective than CSA and SDCSA, since it can reach a higher success rate, better optimal solutions, and a faster convergence. The obtained results show that the proposed MASCSA is a very effective method for the hydrothermal system and wind-hydro-thermal systems.

Performance enhancement of energy extraction capability for fuel cell implementations with improved Cuckoo search algorithm

This paper addresses an improved optimization method to enhance the energy extraction capability of fuel cell implementations. In this study, the proposed method called Dynamic Cuckoo Search Algorithm (DCSA) is tested in a stand-alone fuel cell in order to control the system power under dynamic temperature response. In the operational process, a fuel cell is connected to a load through a dc-dc boost converter, and DCSA is utilized to adjust the switching duration in dc-dc converter by using voltage, current and temperature parameters. In this way, it controls the output voltage to maximize power delivery capability at the demand-side and eliminates the drawback of conventional cuckoo search algorithm (CSA) which cannot change duty cycle under operating temperature variations. In this regard, DCSA shows a significant improvement in terms of system response and achieves a more efficient power extraction than the conventional CSA method. In order to demonstrate the system performance, the stand-alone fuel cell system is constructed in Simulink environment via a processor-in the-loop (PIL) based digital implementation and analyzed by using different optimization methods. In the analysis section, the results of the proposed method are compared with conventional methods (perturb&observe mppt, incremental conductance mppt, and particle swarm optimization). In this context, convergence speed and efficiency analysis for both methods verify that the DCSA gives original results compared to conventional methods.

Cooperative media control parameter optimization of the integrated mixing and paving machine based on the fuzzy cuckoo search algorithm

First, this paper studies the MOH material mixing and paving equipment technology, and quantifies the correlations between each variable through the data fitting based on massive experimental data. Then, with the operation speed as the design variable and yielding the maximum mixing efficiency, minimum slip ratio and highest measuring accuracy as the three performance optimization objectives, an operation speed optimization model for the integrated mixer and paver has been built. Moreover, the principles and workflows of the cuckoo search are investigated, the population diversity and convergence rate of the cuckoo search are improved by using the fuzzy logic, and subsequently the operation speed optimization model is solved using the modified cuckoo search. At last, a simulation test based on MATLAB is carried out for validation. Research results show that the optimization results based on the modified cuckoo search algorithm excels those of frequently-used optimization algorithms such as the conventional cuckoo search and genetic algorithm. Also, the comparison between the cases of the optimized and standard operation speeds show that the mixing efficiency of the integrated mixing and paving machine can grow by 15.3%; the slip ratio drops by 54.2%; the measuring accuracy rises by 18.2%.

Finding Optimal Load Dispatch Solutions by Using a Proposed Cuckoo Search Algorithm

Optimal load dispatch (OLD) is an important engineering problem in power system optimization field due to its significance of reducing the amount of electric generation fuel and increasing benefit. In the paper, an improved cuckoo search algorithm (ICSA) is proposed for determining optimal generation of all available thermal generation units so that all constraints consisting of prohibited power zone (PPZ), real power balance (RPB), power generation limitations (PGL), ramp rate limits (RRL), and real power reserve (RPR) are completely satisfied. The proposed ICSA method performance is more robust than conventional Cuckoo search algorithm (CCSA) by applying new modifications. Compared to CCSA, the proposed ICSA approach can obtain high quality solutions and speed up the solution search ability. The ICSA robustness is verified on different systems with diversification of objective functions as well as the considered constraint set. The results from the proposed ICSA method are compared to other algorithms for comparison. The result comparison analysis indicates that the proposed ICSA approach is more robust than CCSA and other existing optimization approaches in finding solutions with significant quality and shortening simulation time. Consequently, it should lead to a conclusion that the proposed ICSA approach deserves to be applied for finding solutions of OLD problem in power system optimization field.

Fast Converging Cuckoo Search Algorithm to design symmetric FIR filters

Elimination of noise from transmitted signals inevitably incorporated during transmission persisted important task for the researchers from the preliminary days of Signal Processing. Among different techniques proposed regarding this purpose, use of digital filters has become most effectual in multiple ways. Feasible design, lower hardware costs have made the Finite Impulse Response (FIR) filters popular. Among different techniques, using perfect set of filter coefficients to implement FIR filters is most acceptable. Process of determining appropriate set of filter coefficients is characterized as an optimization problem with the objective of minimizing error function. Error function is conceptualized as an approximation function signifying deviancy between the designed filter responses from the ideal filter responses. Present work proposes Fast Converging Cuckoo Search Algorithm to obtain optimized filter coefficients to implement lowpass, highpass and bandpass FIR filters. Responses of the implemented filters using the proposed method are compared with the responses obtained by the filters designed using conventional Cuckoo Search Algorithm and Parks McClellan algorithm.

Hybrid Artificial Neural Network with Meta-heuristics for Grid-Connected Photovoltaic System Output Prediction

This paper presents the performance evaluation of hybrid Artificial Neural Network (ANN) model with selected meta-heuristics for predicting the AC output power fof a Grid-Connected Photovoltaic (GCPV). The ANN has been hybridized with three meta-heuristics, i.e. Cuckoo Search Algorithm (CSA), Evolutionary Programming (EP) and Firefly Algorithm (FA) separately. These meta-heuristics were used to optimize the number of neurons, learning rate and momentum rate such that the Root Mean Square Error (RMSE) of the prediction was minimized during the ANN training process. The results showed that CSA had outperformed EP and FA in producing the lowest RMSE. Later, Mutated Cuckoo Search Algorithm (MCSA) was introduced by incorporating Gaussian mutation operator in the conventional CSA. Further investigation showed that MSCA performed better prediction when compared with the conventional CSA in terms of RMSE and computation time.

Hardware efficient FIR filter design using Global Best Steered Quantum Inspired Cuckoo Search Algorithm

In this paper, a new algorithm namely Global Best Steered Quantum Inspired Cuckoo Search Algorithm (GQICSA) is proposed for obtaining optimized set of coefficients to implement Finite Impulse Response (FIR) Filter. Adder cost of a filter is estimated after quantizing the filter coefficients followed by Common Sub-expression Elimination (CSE). We found from the simulation results that reduction in word length of coefficients does not make the filters fail to achieve the ideal frequency response. Moreover, filters developed using GQICSA outperform the benchmark filters designed by Parks McClellan Algorithm in terms of stopband attenuation. Analysis of the results reveal that GQICSA not only improves over various conventional algorithms including Cuckoo Search Algorithm (CSA), it also surpasses modified version of CSA, Quantum Inspired CSA (QICSA) updated using quantum principles, for optimizing filter coefficients to design lower hardware costing filter without compromising the filter responses and efficiency. GQICSA also provides significant improvements compared to CSA and QICSA in terms of stopband attenuation and execution time for higher order filter design. Efficiency of GQICSA over QICSA and conventional CSA is also proved with 16 benchmark functions.

Modified Cuckoo Search Algorithm: A Novel Method to Minimize the Fuel Cost

Economic load dispatch (ELD) is an important optimization problem for operating and controlling modern power systems, and if ELD is effectively executed, power systems work stably and economically. The main objective of this paper is to develop a novel method to solve the ELD with the purpose of minimizing the total fuel cost of all available generating units while requirements are to satisfy all constraints regarding thermal units, generators, and transmission power networks. The proposed high performance cuckoo search algorithm (HPCSA) is developed from the efficient technique for the second new solution generation of conventional cuckoo search algorithm (CCSA), called adaptive mutation technique. This proposed technique diversifies the local search ability based on a new comparison criterion. The HPCSA is verified on difference systems under special conditions, namely the 10-unit system with multi fuels, 15-unit system considering prohibited operating zones, and three IEEE systems with 30, 57, and 118 buses considering transmission power network constraints. The specific evaluation of the HPCSA is compared to that of Lagrange optimization-based methods (LMS), neural network-based methods (NNMS), CCSA, and other popular methods such as Particle swarm optimization (PSO) variants, Differential evolution (DE) variants, Genetic Algorithm (GA) variants, and state-of-the-art methods. In comparison with CCSA, the proposed method is always more effective and more robust since the proposed method can find most solutions with better quality and faster convergence speed. In comparison with LMS and NNMS, the proposed method can also find solutions with approximate or equal quality. In comparison with popular methods and state-of-the-art methods, the proposed method has more potential since it can reach faster convergence to valid solutions with approximate or better quality. Consequently, it can be concluded that the proposed HPCSA is an effective optimization tool for dealing with ELD problems.

An effectively adaptive selective cuckoo search algorithm for solving three complicated short-term hydrothermal scheduling problems

This paper proposes an effectively adaptive selective cuckoo search algorithm (ASCSA) for solving short-term hydrothermal scheduling problems with available water constraint, reservoir volume constraints, and transmission network constraints. The proposed ASCSA is a newly improved version of the conventional cuckoo search algorithm to enhance the solution quality and reduce the maximum number of iterations based on two new techniques including the new ratio of the difference between the fitness function values and the integration of solutions into one group. The effectiveness of ASCSA has been validated via eight hydrothermal systems, in which the last two systems consisting of the IEEE 30-bus and IEEE 118-bus systems are considered with a set of constraints in the transmission network. To investigate the performance of ASCSA, several algorithms are also implemented in the paper such as conventional cuckoo search algorithm, modified cuckoo search algorithm, particle swarm optimization, global vision of particle swarm optimization with inertia weight, differential evolution, and improved differential evolution. From result comparisons of the test systems, the proposed ASCSA method has obtained lower total costs than other methods implemented for solving the problems. Therefore, the proposed ASCSA is a very efficient and favorable method for solving the considered hydrothermal scheduling problems.

The application of an effective cuckoo search algorithm for optimal scheduling of hydrothermal system considering transmission constraints

This paper presents an implementation of an effective cuckoo search algorithm (ECSA) for solving hydrothermal scheduling (ST-FH-HTS) problems considering transmission power losses, nonconvex fuel cost function of thermal units, and transmission grid constraints such as the voltage of load buses, voltage of generator buses, capacity of transmission lines. The ECSA method has been developed based on the conventional cuckoo search algorithm (CCSA) which is a recently developed meta-heuristic algorithm inspired from the obligate brood parasitism of some cuckoo species by laying their eggs in the nests of other birds of other species for solving optimization problems. In the ECSA method, new eggs generated via Levy flights are replaced partially, and the newly generated eggs are then evaluated and ranked at once. Moreover, there is a boundary by the best solution technique proposed for replacing the invalid dimension in order to improve convergence rate and performance. The performance of ECSA has been investigated via comparisons with other methods by testing on eleven systems. In addition, the ECSA and other popular meta-heuristic algorithms such as CCSA, conventional particle swarm optimization, conventional differential evolution, and conventional Bat algorithm have been tested on a large-scale system with 50 thermal units and four hydrounits considering constraints from an IEEE 118-bus transmission line grid. The result comparisons from ECSA with other methods for 12 test systems have revealed that ECSA method is very efficient for solving ST-FH-HTS problems. Therefore, the ECSA can be a favorable method for solving the ST-FH-HTS problems.

A Novel Algorithm for Optimal Operation of Hydrothermal Power Systems under Considering the Constraints in Transmission Networks

This paper proposes an effective novel cuckoo search algorithm (ENCSA) in order to enhance the operation capacity of hydrothermal power systems, considering the constraints in the transmission network, and especially to overcome optimal power flow (OPF) problems. This proposed algorithm is developed on the basis of the conventional cuckoo search algorithm (CSA) by two modified techniques: the first is the self-adaptive technique for generating the second new solutions via discovery of alien eggs, and the second is the high-quality solutions based on a selection technique to keep the best solutions among all new and old solutions. These techniques are able to expand the search zone to overcome the local optimum trap and are able to improve the optimal solution quality and convergence speed as well. Therefore, the proposed method has significant impacts on the searching performances. The efficacy of the proposed method is investigated and verified using IEEE 30 and 118 buses systems via numerical simulation. The obtained results are compared with the conventional cuckoo search algorithm (CCSA) and the modified cuckoo search algorithm (MCSA). As a result, the proposed method can overcome the OPF of hydrothermal power systems better than the conventional ones in terms of the optimal solution quality, convergence speed, and high success rate.

Planning Optimization of the Distributed Antenna System in High-Speed Railway Communication Network Based on Improved Cuckoo Search

The network planning is a key factor that directly affects the performance of the wireless networks. Distributed antenna system (DAS) is an effective strategy for the network planning. This paper investigates the antenna deployment in a DAS for the high-speed railway communication networks and formulates an optimization problem which is NP-hard for achieving the optimal deployment of the antennas in the DAS. To solve this problem, a scheme based on an improved cuckoo search based on dimension cells (ICSDC) algorithm is proposed. ICSDC introduces the dimension cell mechanism to avoid the internal dimension interferences in order to improve the performance of the algorithm. Simulation results show that the proposed ICSDC-based scheme obtains a lower network cost compared with the uniform network planning method. Moreover, ICSDC algorithm has better performance in terms of the convergence rate and accuracy compared with the conventional cuckoo search algorithm, the particle swarm optimization, and the firefly algorithm.

Improved Cuckoo Search Algorithm for Nonconvex Hydrothermal Scheduling with Volume Constraint

This paper presents an improved cuckoo search algorithm (ICSA) for solving the short-term hydrothermal scheduling (ST-HTS) problem. In the proposed ICSA, the initial eggs are first classified into two groups including top group and abandoned group and each egg in the two groups is then newly generated via Levy flights. The two new egg groups will be integrated into only one group before ranking and evaluating to choose the best egg, which will be used to generate new eggs via the action of discovery of alien eggs in the host bird’s nest. Moreover, the fraction of the alien eggs to be abandoned will be adaptive at each iteration instead of a fixed value during the search process like the conventional Cuckoo Search Algorithm (CSA). The performance of the proposed ICSA has been tested on two different systems and the obtained results have been compared to those from many methods including CSA, modified CSA and other methods in the literature in terms of solution quality. The result comparison has revealed that the proposed ICSA is very efficient for solving the ST-HTS with reservoir storage constraints.DOI: http://dx.doi.org/10.5755/j01.eie.23.3.12657