A comprehensive overview on variants of CUCKOO search algorithm and applications

Cuckoo search (CS) is widely used to solve many optimisation problem, which is a biologically inspired the brood parasitic behaviour of a type of cuckoos and the Levy flights behaviour of some animals. However, it has been demonstrated to easily get trapped into local optimal solutions and slow convergence speed. Therefore, an improved adaptive cuckoo search (IACS) optimisation algorithm is proposed in this article. Two adaptive strategies based on the population feature and iteration information feedback which are integrated into the CS algorithm to adjust the parameters pa and α0. We compared the proposed algorithm to CS and five variants on the 30 benchmark functions proposed in CEC 2014. In addition, the proposed algorithm and CS are integrated into support vector machine (SVM) for classification. Experimental results certify that the modified algorithm is superior to the CS for most optimisation problems and has better performance than the other variants of CS algorithm.

Block Matching (BM) is considered to be the most efficient technique for Motion Estimation (ME) in Video Compression. Many algorithms have been developed for BM which includes classical as well as metaheuristic methods. Harmony Search (HS), Differential Evolution (DE), Artificial Bee Colony (ABC), Particle Swarm Optimization (PSO) are the metaheuristic methods used for this purpose. This paper explores a way to formulate a BM algorithm by using Cuckoo Search — a metaheuristic optimization algorithm which is mainly inspired by the breeding behavior of some cuckoo species which shows an obligate brood parasitism. Levy flight behavior of some fruit flies and birds is also incorporated in Cuckoo Search.

The satellite image segmentation is a key area for current research and numerous work has been done for exploration of this area. The nature inspired optimization algorithms are very promising with image segmentation techniques to provide a platform for processing of satellite images. In this paper a literature review of different nature based optimization algorithms such as modified artificial bee colony (MABC) algorithm, ABC algorithm, particle swarm optimization (PSO), Darwinian PSO, genetic algorithm (GA), Wind driven optimization (WDO) and cuckoo search(CS) using different objective functions has been discussed to find the optimized multilevel thresholds. These nature influenced optimization methods and their performances are compared using different objective functions for optimal multilevel thresholding. The comparative study shows that the MABC algorithm and different variants of CS algorithm are very strong and accurate in results generating using image segmentation. Both methods search multilevel thresholds very efficiently and correctly, and in MABC an improved bee’s search solution are used and in CS the cuckoo search solution are used.

The cuckoo search (CS) algorithm, which is a new meta-heuristic optimization algorithm, is an efficient approach to solve continuous function optimization problems. Normally, the parameters of the CS are fixed constants. This may result in affecting the algorithm efficiency. To cope with this issue, we properly tune the parameters of the CS and propose an adaptive cuckoo search (ACS) algorithm to enhance the convergence rate and accuracy of the CS. Then, we validate the proposed ACS against test functions and compare its performance with those of particle swarm optimization (PSO), improved cuckoo search algorithm (ICS) and CS by simulation. The simulation results demonstrate that the ACS obtains some solutions better than those obtained by PSO, ICS and CS.

This paper anticipates to frame a hybrid Seagull Optimization Algorithm - Cuckoo Search (SOA-CS) algorithm for objective function minimization. The migration and attacking strategies of Seagulls are complimented by the social breeding behaviour of cuckoo birds. In addition, the thought of Lévy flight walking methodology is also inherited. Twenty-three standard benchmark test functions are used for validation. To figure out the outcomes - the proposed algorithm converges faster and provides better results over both the SOA and CS algorithms.

Cloud computing is popular among industries, academia, and government to supply reliable and scalable computational power. High-speed networks in cloud data centers connect Virtual machines with Physical Machines. Virtualization assists the cloud service providers to manage resources effectively but unoptimized and inefficient services degrade the performance of the system. The scheduling architecture of cloud computing includes Physical Machines (PMs), Virtual Machines (VMs) and the allocation and migration policy of the VMs over the PMs. The overutilized PMs get few migrations and this paper introduces novel behavior of VM selection from overutilized PMs using Swarm intelligence. The evaluation of the proposed algorithm architecture is compared with another state-of-the-art optimization algorithm from the same series. The evaluation has been done on the base of Quality of Service (QoS) parameters, such as Service Level Agreement (SLA) violation, and energy consumption against various load variation scenarios to support elasticity. The proposed algorithm has outperformed other techniques by considerable margin in terms of QoS, and the details are presented in the results section. The simulation results demonstrate that the proposed technique exhibits 6.3% and 6.7% enhancement in terms of reduced energy consumption compared to both Cuckoo Search (CS) and general Dragonfly (DF) techniques, and 3% decrease in SLA violations in comparison to current methods. Additionally, the results reveal an 11% enhancement in VM migration compared to existing approaches.

Cuckoo search (CS) is an efficient bio-inspired algorithm and has been studied on global optimisation problems extensively. It is expert in solving complicated functions but converges slowly. Another optimisation algorithm, covariance matrix adaption evolution strategy (CMA_ES) can speed up the convergence rate via the self-adaptative mutation distribution and the cumulative evolution path, whereas it performs badly in complex functions. Therefore, in this paper, we devise a hybridisation of CS and CMA_ES and name it CS_CMA, to improve performance for the different optimisation problems. An evolved population is initialised at the beginning of iteration, using the information of previous evolution. Self-adaptive parameter adjustments are employed through the successful parameter values. To validate the performance of CS_CMA, comparative experiments are conducted based on seven high-dimensional benchmark functions provided for CEC 2008 and an engineering optimisation problem chosen from CEC' 2011, and computational results demonstrate that CS_CMA outperforms other competitor algorithms.

This paper proposes a novel methodology based on the recent metaheuristic grasshopper optimization algorithm (GOA) for extracting the maximum power from a wind energy system operated under variable speed conditions. The proposed system comprises a permanent magnet synchronous generator operated via a wind turbine, a dc-dc boost converter, a three-phase diode rectifier, and a maximum power point tracker (MPPT) designed via the proposed GOA. The proposed MPPT tunes the duty cycle of the boost converter to extract the maximum power from the wind energy system. The proposed system is built in Matlab/Simulink, different operating conditions are studied, and the results are compared with other programed metaheuristic approaches like Particle Swarm Optimization, Cuckoo Search, and Crow Search Algorithm. Additionally, the proposed GOA-MPPT is investigated using actual collected data for wind speed in four different locations in the Al-Jouf region in the northern Saudi Arabia, which are Sakaka, Dumat Al-Jandal, Qurayyat, and Tabarjal. The obtained results confirmed the superiority and reliability of the proposed MPPT compared to the others in enhancing the extracted power from the wind energy system.

This study proposes a solution to the optimal power flow (OPF) in power systems incorporating wind power. The wind generated electricity's cost modelling is presented which owes to the stochastic nature of wind speed based on the Weibull probability density function. The wind associated costs comprise of (i) reserve cost arising out of overestimation of wind farm output and (ii) penalty cost for not using total available wind power which are included in the conventional OPF. Furthermore, the Q‐V formulation of induction generator is deduced which is incorporated through a modification in the conventional load flow solution. A modified cuckoo search optimisation technique is employed and compared with modified particle swarm optimisation for solving OPF with wind power involved. A modified IEEE 30‐bus and IEEE 57‐bus systems are used as test systems and it is observed that the cuckoo search performs better than particle swarm optimisation.

Hippopotamus algorithm for optimal sizing of hybrid renewable energy system

Several optimization problems from various types of applications have been efficiently resolved using available meta-heuristic algorithms such as Particle Swarm Optimization and Genetic Algorithm. Recently, many meta-heuristic optimization techniques have been extensively reported in the literature. Nevertheless, there is still room for new optimization techniques and strategies since, according to the literature, there is no meta-heuristic optimization algorithm that may be considered as the best choice to cope with all modern optimization problems. This paper introduces a novel meta-heuristic optimization algorithm named Dynamic Group-based Optimization Algorithm (DGCO). The proposed algorithm is inspired by the cooperative behavior adopted by swarm individuals to achieve their global goals. DGCO has been validated and tested against twenty-three mathematical optimization problems, and the results have been verified by a comparative study with respect to state-of-the-art optimization algorithms that are already available. The results have shown the high exploration capabilities of DGCO as well as its ability to avoid local optima. Moreover, the performance of DGCO has also been verified against five constrained engineering design problems. The results demonstrate the competitive performance and capabilities of DGCO with respect to well-known state-of-the-art meta-heuristic optimization algorithms. Finally, a sensitivity analysis is performed to study the effect of different parameters on the performance of the DGCO algorithm.

Optimal charging of fractional-order circuits with Cuckoo search

Drill path optimization problem is an important problem in holes drilling with computer numerically controlled (CNC) machine. Due to the exponential increase in the number of possible solutions when the number of holes to be drilled increase, the metaheuristic optimization algorithm seems to be a good choice in solving this type of optimization problem. This paper presents a Kalman Filter approach in solving printed circuit board (PCB) routing problem by using the Simulated Kalman Filter (SKF) algorithm. The experimental results are compared with those obtained by swarm intelligence approach, which are the Particle Swarm Optimization (PSO) variants, Ant Colony System (ACS) and Cuckoo Search (CS). The implementation proves to be effortless with good global convergence capability.

The stochastic nature of wind energy generation introduces uncertainties and risk in generation schedules computed using optimal power flow (OPF). This risk is quantified as expected energy not served (EENS) and computed via an error distribution found for each hourly forecast. This thesis produces an accurate method of estimating EENS that is also suitable for real-time OPF calculation. This thesis examines two statistical predictive models used to forecast hourly production of wind energy generators (WEGs), Markov chain model, and auto-regressive moving-average (ARMA) model, and their effects on EENS. Persistence model is used as a benchmark for comparison. For persistence and ARMA models, both Gaussian and Cauchy error distributions are used to compute EENS via a closed-form solution that reduces computational complexity. Markov chain and ARMA both provide accurate forecasts of WEG power generation though Markov Chain model performs significantly better. The Markov chain model also produces the most accurate EENS estimate of the three models.

The Cuckoo Search (CS) algorithm is an effective swarm intelligence optimization algorithm whose important developments were presented by Yang and Deb in 2009. The CS algorithm has been used in many applications to solve optimization problems. This paper describes an overview of the applications of CS in the scope of image processing to solve optimization problems for the image during the years 2015-2021. The main categories reviewed that used CS in the field of image processing are: image segmentation, image optimization, image noise removal, image classification, feature extraction in images, image clustering and edge detection. The aim of this paper is to provide an overview and summarize the literature review of applying CS algorithm in these categories in order to extract which categories that applied this algorithm more than others. From this review we conclude that CS was mostly applied in the image segmentation category to optimize the threshold search.