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Cuckoo Search (CS) is a popular algorithm used to solve numerous challenging problems. In the present work, a novel variant of CS is presented to eliminate its shortcomings. The proposed algorithm is hybridized with the naked mole rat algorithm (NMRA) to enhance the exploitative behavior of CS, and is called Mutated Adaptive Cuckoo Search Algorithm (MaCN). This new algorithm has self-adaptive properties and its key feature is to divide the solutions into multiple sections, which are often called sub-swarms. In addition, a bare-bones search mechanism is also added to enhance exploration. The use of adaptive inertia weights helps optimize the switching probability, an important CS parameter that helps to achieve a balanced operation. The proposed MaCN algorithm is tested on CEC 2005 and CEC 2014 benchmark problems. Comparative studies showed that MaCN delivers promising results in solving CEC competition benchmark problems compared to JADE, success history-based adaptive DE (SHADE), LSHADE-SPACMA and self-adaptive DE (SaDE), among others. In addition to numerical benchmarks, MaCN is used to solve the industrial engineering frame structure and a comparison with hybridization of particle swarm with passive congregation (PSOPC), shuffled frog leaping algorithm hybrid with invasive weed optimization (SFLAIWO), particle swarm ant colony optimization (PSOACO), early strategy with DE (ES-DE), and others show its superiority. In addition, the Wilcoxon rankum and the Freidmann test statistically prove the significance of the proposed MaCN algorithm. MaCN was found to score first rank for the benchmarks. The application of the MaCN algorithm to solve the design problems of the suggests that the best new results are obtained for all test cases.

Environmental issues, particularly air pollution, are a matter of concern for people all around the world. PM2.5 levels that are too high harm people’s physical and mental health. For government air pollution control, more accurate PM2.5 concentration predictions are critical. In this paper, we explored the relationship between pollutants (PM10, SO2, NO2, O3, CO) and meteorological factors (atmospheric pressure, relative humidity, air temperature, wind speed, wind direction, cumulative precipitation) that affect the generation and transmission of PM2.5. To better predict the concentration of PM2.5, we innovatively combined principal component analysis (PCA) and clustering methods to extract pollutant variables and patterns as important PM2.5 concentration predictors of different models such as support vector regression (SVR), multivariate nonlinear regression (MNR), and artificial neural network (ANN). Compared to MNR and ANN models, SVR presented better prediction accuracy. Moreover, cuckoo search (CS), cross-validation (CV), and particle swarm optimization (PSO) algorithms were used to further optimize the parameters in the process of SVR. And to evaluate the above PM2.5 concentration prediction results, we introduced several evaluating indicators including root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and person correlation coefficient (R) between predicted and measured values. The obtained results confirmed that when the pollutant data was divided into three patterns, the best prediction accuracy was achieved by the CS-SVR model.

Cuckoo search algorithm is one of the most prominent meta-heuristic optimization algorithms which is applied to various applications. The discovery probability is the one and the only tuning parameter of the cuckoo search algorithm. The physical meaning of this parameter contradicts its implementation in the standard algorithm. Therefore, this study concerns the correction to the definition and implementation of the cuckoo search algorithm to resolve this conflict. Moreover, a novel algorithm called double exponential cuckoo search is proposed, in which the discovery probability became adaptive based on the concept of the double Mersenne numbers. The proposed algorithm is compared to nine other variants to find the best variant that makes the discovery probability adaptive. All the variants are compared and tested on 30 and 50 dimensions of CEC2017 benchmark functions. The results have been statistically proved using the sign test, Wilcoxon signed-rank test, and Friedman test. Moreover, multiple graphical methods are also used to visualize the median performance such as Violin plots and mean convergence graphs. Simulation results prove the superior performance of the proposed algorithm over all other variants.

One of the most significant and difficult challenges in solar systems is determining the maximum power point. This process becomes more difficult when the array of solar modules is subjected to non-uniform shade. A unique hybrid maximum power point tracking (MPPT) approach is presented in this research. The suggested approach, cuckoo search–perturb observe, is created by combining the traditional perturb & observe (P&O) and cuckoo search algorithms (CSPO). A hybrid algorithm has been developed to capitalize on the benefits of both algorithms while overcoming their respective disadvantages. The proposed algorithm attempts to incorporate the most prominent aspects of both algorithms while also attempting to address the drawbacks of both algorithms and improving the accuracy of the new approach by resolving the issue of P&O getting stuck on local maxima during non-uniform irradiance and reducing the resource-intensive approach of cuckoo search by limiting its number of iterations. Furthermore, the suggested approach locates the maximum power point using just voltage sensors, decreasing sensor requirements. The simulation findings for maximum power point tracking performance are also presented.

Many optimization problems in science and engineering are highly nonlinear and thus require sophisticated optimization techniques to solve. Traditional techniques such as gradient-based algorithms are mostly local search methods and often struggle to cope with such challenging optimization problems. Recent trends tend to use nature-inspired optimization algorithms. The standard cuckoo search (CS) is an optimization algorithm based on a single cuckoo species and a single host species. This work extends the standard CS by using the successful features of the cuckoo-host co-evolution with multiple interacting species. The proposed multi-species cuckoo search (MSCS) intends to mimic the co-evolution among multiple cuckoo species that compete for the survival of the fittest. The solution vectors are encoded as position vectors. The proposed algorithm is then validated by 15 benchmark functions as well as five nonlinear, multimodal case studies in practical applications. Simulation results suggest that the proposed algorithm can be effective for finding optimal solutions and all optimal solutions are achievable in the tested cases. The results for the test benchmarks are also compared with those obtained by other methods such as the standard cuckoo search and genetic algorithm. The comparison has demonstrated the efficiency of the present algorithm. Based on numerical experiments and case studies, we can conclude that the proposed algorithm can be more efficient in most cases. Therefore, the proposed approach can be a very effective tool for solving nonlinear global optimization problems.

With the paradigm of the Internet of things, each object in the physical world can be remotely identified, controlled, and located through networks. Thanks to their low cost and their small form, the Radio frequency identification (RFID) tags are frequently used to tag objects . The tags or objects are often distributed in large geographic areas. Due to the limit of the interrogation range of RFID readers, multiple readers should be deployed to read the information stored on all tags. The major challenge in an RFID network design is to find the optimal placement and parameters of readers in order to meet the essential requirements of an RFID system such as coverage, load balance and interference between readers. This challenge has led to a new research area known in the literature as the RFID network planning problem. This problem is characterized by a large number of constraints as well as numerous objectives and it proves to be NP-hard. In this paper, we develop a novel optimization algorithm, namely the self adaptive cuckoo search (SACS) algorithm, to solve this complex problem. In the SACS algorithm, the control parameters of the cuckoo search (CS) algorithm are adjusted dynamically in real time. The self-adaptation phenomenon allows the evolutionary algorithm to be more flexible and closer to natural evolution. The experimental results on 13 standard benchmark functions demonstrate that the proposed algorithm is more efficient than five adaptive variants of the CS algorithm. In the second part of the paper, the SACS algorithm is also used to solve three difficult RFID network planning instances. The simulation studies show that the SACS algorithm obtains better solutions for the RFID network planning problem than the original CS, four adaptive CS variants, the GA and the PSO in terms of optimization and robustness. To test the effectiveness of the SACS algorithm on a real problem, a case study is carried out.

The standard cuckoo search algorithm (SCSA) is an intelligent population optimization algorithm, which is also a heuristic search algorithm. The advantages of the SCSA (such as its convenient operation, heuristic searching, etc.) make it easy to find an optimal solution and maintain a wide searching range. However, the SCSA also has some drawbacks, such as long searching time, and the ease of falling on a local optimum. In order to solve the problems existing with SCSA, in this paper, an improved standard cuckoo search algorithm (ISCSA) was studied, which includes chaotic initialization and a Gaussian disturbance algorithm. As a case study, taking economic, social and ecological benefits as the objective function, multi-objective water resources optimal allocation models were constructed in Xianxiang Region, China. The ISCSA was applied to solve the water allocation models and a multi-objective optimal water supply scheme for Xinxiang region was obtained. Water resources optimal allocation schemes for the planning level year (2025) for 12 water supply sub-regions were predicted. A desirable eco-environment and other benefits were achieved using the studied methods. The results show that the ISCSA has obvious advantages in the solution of water resources optimal allocation and planning.

Spam mail classification has been playing a vital role in recent days due to the uncontrollable growth happening in the electronic media. Literature presents several algorithms for email spam classification based on classification methods. In this paper, we propose a spam classification framework using S-Cuckoo and hybrid kernel based support vector machine (HKSVM). At first, the features are extracted from the e-mails based on the text as well as the image. For the textual features, TF-term frequency is used. For the image dependent features, correrlogram and wavelet moment are taken. The hybrid features have then high dimension so the optimum features are identified with the help of hybrid algorithm, called S-Cuckoo search. Then, the classification is done using proposed classifier HKSVM model which is designed based on the hybrid kernel by blending three different kernel functions and then it is used in the SVM classifier. The additional features provided based on image and the modification of SVM classifier provides significant improvement as compared with existing algorithms. The spam classification performance is measured by db1 (combining bare-ling spam and Spam Archive corpus) and db2 (combining lemm-ling spam and Spam Archive corpus). Experimental results show that the proposed spam classification framework has outperformed by having better accuracy of 97.235% when compared with existing approach which is able to achieve only 94.117%.

Short Term Load Forecasting (STLF) is the projection of system load demands for the next day or week. Because of its openness in modeling, simplicity of implementation, and improved performance, the ANN-based STLF model has gained traction. The neural model consists of weights whose optimal values are determined using various optimization approaches. This paper uses an Artificial Neural Network (ANN) trained using multiple hybrid techniques (HT) such as Back Propagation (BP), Cuckoo Search (CS) model, and Bat algorithm (BA) for load forecasting. Here, a thorough examination of the various strategies is taken to determine their scope and ability to produce results using different models in different settings. The simulation results show that the BA-BP model has less predicting error than other techniques. However, the Back Propagation model based on the Cuckoo Search method produces less inaccuracy, which is acceptable.

Integrating heterogeneous biological-inspired strategies and mechanisms into one algorithm can avoid the shortcomings of single algorithm. This article proposes an integrated cuckoo search optimizer (ICSO) for single objective optimization problems, which incorporates the multiple strategies into the cuckoo search (CS) algorithm. The paper also considers the proposal of multi-objective versions of ICSO called MOICSO. The two algorithms presented in this paper are benchmarked by a set of benchmark functions. The comprehensive analysis of the experimental results based on the considered test problems and comparisons with other recent methods illustrate the effectiveness of the proposed integrated mechanism of different search strategies and demonstrate the performance superiority of the proposed algorithm.