Cuckoo Search Optimization- A Review
TL;DR: The cuckoos behaviour & their egg laying strategy in the nests of other host birds is explained and a proper strategy for tuning the cuckoo search parameters is defined.
About: This article is published in Materials Today: Proceedings.The article was published on 2017-01-01. It has received 127 citations till now. The article focuses on the topics: Cuckoo search & Cuckoo.
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TL;DR: This paper aims to undertake a comprehensive review on meta-heuristic algorithms and related variants which have been applied on PV cell parameter identification and presents some perspectives and recommendations for future development.
212 citations
TL;DR: In this paper, an extreme learning machine integrated with cuckoo search algorithm was developed to predict and optimize the process parameters of microwave irradiation-assisted transesterification process conditions.
190 citations
TL;DR: Dijkstra ’s Algorithm (DA) is considered a benchmark solution and Constricted Particle Swarm Optimization (CPSO) is found performing better than other meta-heuristic approaches in unknown environments.
92 citations
TL;DR: Multiple hybrid machine-learning models were developed to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models to confirm the ability of metaheuristic algorithms to improve model performance.
Abstract: In this study, we developed multiple hybrid machine-learning models to address parameter optimization limitations and enhance the spatial prediction of landslide susceptibility models. We created a geographic information system database, and our analysis results were used to prepare a landslide inventory map containing 359 landslide events identified from Google Earth, aerial photographs, and other validated sources. A support vector regression (SVR) machine-learning model was used to divide the landslide inventory into training (70%) and testing (30%) datasets. The landslide susceptibility map was produced using 14 causative factors. We applied the established gray wolf optimization (GWO) algorithm, bat algorithm (BA), and cuckoo optimization algorithm (COA) to fine-tune the parameters of the SVR model to improve its predictive accuracy. The resultant hybrid models, SVR-GWO, SVR-BA, and SVR-COA, were validated in terms of the area under curve (AUC) and root mean square error (RMSE). The AUC values for the SVR-GWO (0.733), SVR-BA (0.724), and SVR-COA (0.738) models indicate their good prediction rates for landslide susceptibility modeling. SVR-COA had the greatest accuracy, with an RMSE of 0.21687, and SVR-BA had the least accuracy, with an RMSE of 0.23046. The three optimized hybrid models outperformed the SVR model (AUC = 0.704, RMSE = 0.26689), confirming the ability of metaheuristic algorithms to improve model performance.
82 citations
TL;DR: A systematic review and meta-analysis of the related studies and recent developments on Backtracking search optimisation algorithm and indicates that BSA is statistically superior than the aforementioned algorithms in solving different cohorts of numerical optimisation problems such as problems with different levels of hardness score, problem dimensions, and search spaces.
59 citations
Cites background from "Cuckoo Search Optimization- A Revie..."
...BSA and cuckoo search (CS) are both population-based algorithms [208]; therefore, the initial population of BSA can be set using CS....
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References
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TL;DR: Empirical results reveal that the problem solving success of the CK algorithm is very close to the DE algorithm and the run-time complexity and the required function-evaluation number for acquiring global minimizer by theDE algorithm is generally smaller than the comparison algorithms.
Abstract: In this paper, the algorithmic concepts of the Cuckoo-search (CK), Particle swarm optimization (PSO), Differential evolution (DE) and Artificial bee colony (ABC) algorithms have been analyzed. The numerical optimization problem solving successes of the mentioned algorithms have also been compared statistically by testing over 50 different benchmark functions. Empirical results reveal that the problem solving success of the CK algorithm is very close to the DE algorithm. The run-time complexity and the required function-evaluation number for acquiring global minimizer by the DE algorithm is generally smaller than the comparison algorithms. The performances of the CK and PSO algorithms are statistically closer to the performance of the DE algorithm than the ABC algorithm. The CK and DE algorithms supply more robust and precise results than the PSO and ABC algorithms.
656 citations
TL;DR: A new robust optimisation algorithm, which can be regarded as a modification of the recently developed cuckoo search, is presented and shows a high convergence rate to the true global minimum even at high numbers of dimensions.
Abstract: A new robust optimisation algorithm, which can be regarded as a modification of the recently developed cuckoo search, is presented. The modification involves the addition of information exchange between the top eggs, or the best solutions. Standard optimisation benchmarking functions are used to test the effects of these modifications and it is demonstrated that, in most cases, the modified cuckoo search performs as well as, or better than, the standard cuckoo search, a particle swarm optimiser, and a differential evolution strategy. In particular the modified cuckoo search shows a high convergence rate to the true global minimum even at high numbers of dimensions.
508 citations
TL;DR: An improved and discrete version of the Cuckoo Search (CS) algorithm is presented to solve the famous traveling salesman problem (TSP), an NP-hard combinatorial optimisation problem.
Abstract: In this paper, we present an improved and discrete version of the Cuckoo Search (CS) algorithm to solve the famous traveling salesman problem (TSP), an NP-hard combinatorial optimisation problem. CS is a metaheuristic search algorithm which was recently developed by Xin-She Yang and Suash Deb in 2009, inspired by the breeding behaviour of cuckoos. This new algorithm has proved to be very effective in solving continuous optimisation problems. We now extend and improve CS by reconstructing its population and introducing a new category of cuckoos so that it can solve combinatorial problems as well as continuous problems. The performance of the proposed discrete cuckoo search (DCS) is tested against a set of benchmarks of symmetric TSP from the well-known TSPLIB library. The results of the tests show that DCS is superior to some other metaheuristics.
403 citations
TL;DR: This research is the first application of the cuckoo search algorithm (CS) to the optimization of machining parameters in the literature, and the results demonstrate that the CS is a very effective and robust approach for the optimization for machining optimization problems.
Abstract: In this research, a new optimization algorithm, called the cuckoo search algorithm (CS) algorithm, is introduced for solving manufacturing optimization problems. This research is the first application of the CS to the optimization of machining parameters in the literature. In order to demonstrate the effectiveness of the CS, a milling optimization problem was solved and the results were compared with those obtained using other well-known optimization techniques like, ant colony algorithm, immune algorithm, hybrid immune algorithm, hybrid particle swarm algorithm, genetic algorithm, feasible direction method, and handbook recommendation. The results demonstrate that the CS is a very effective and robust approach for the optimization of machining optimization problems.
376 citations
261 citations