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Simulation and Optimization of Hybrid Renewable Energy Systems
Published in Yatish T. Shah, Hybrid Power, 2021
Cuckoo search is a new metaheuristic algorithm that solves optimization problems, which is based on the obligate brood parasitic behavior of some cuckoo species in combination with the Lévy flight behavior of some birds and fruit flies. The sizing optimization can be figured as a multi-objective problem with economic, technical, and environmental constraints. Nadjemi et al [135] proposed an updated state-of-the-art optimization techniques used for the sizing and energy management of PWHS which is based on a new sizing approach of cuckoo search algorithm for grid application systems. Multi-objective approach
Cuckoo Search Algorithm
Published in A Vasuki, Nature-Inspired Optimization Algorithms, 2020
Cuckoo search (CS) is a metaheuristic optimization algorithm based on the obligate parasitic breeding behavior of some species of cuckoo birds. It also inculcates the Levy flight movement exhibited by some animals, birds, and insects, including cuckoos. The cuckoo search optimization algorithm has been developed based on three assumptions: The number of host nests in which a cuckoo can lay its egg is fixed, represented by the population size N. Let ph ∈ [0, 1] be the probability that the host bird discovers the cuckoo egg laid in its nest. Once discovered, the host bird either evicts the egg or abandons the nest and builds a new one. Therefore, ph is the fraction of the N nests that are either abandoned or replaced with new ones by the host bird. This characteristic is equivalent to abandoning solutions that have lesser fitness values and replacing them with new and better solutions. Each host nest represents one possible candidate solution to the problem. A cuckoo bird lays only one egg in a randomly chosen host nest at any instant of time. The nests with high-quality eggs have higher fitness values and hence survive to the next generation.
Introduction to Wireless Sensor Networks
Published in Huynh Thi Thanh Binh, Nilanjan Dey, Soft Computing in Wireless Sensor Networks, 2018
G. Bhanu Chander, G. Kumaravelan
Area Coverage: WSNs are constructed with limited number of sensor nodes. Area coverage tends to spread across the entire region with a minimum quantity of homogeneous sensor nodes. Classically, WSNs consists of two key processes: (1) area coverage and (2) computational complexity. The first one is after organizing discretely in pre-defined section. Sensor nodes account for environmental facts endlessly. Furthermore, sensed substances are sent to a base station by the use of a communication component, either straightforwardly or through additional nodes, even though this category of sensor uses a lot of energy. We know that the broadcast charge is higher than the computation charge in WSNs. First-rate area coverage optimization procedure in WSNs accomplishes enhanced exposure superiority within a smaller computational time. Improved cuckoo search is one of the evolutionary optimization algorithms; modeled after the cuckoo bird which lays its eggs on the nests of other bird species. If the entertainer or host bird is proficient enough to figure out the intrusion, it will either pitch the alien eggs aside or pitch out its solitary nest and assemble a fresh one. Virtual force-based coverage optimization, pure genetic algorithm, Monte Carlo GA, optimal genetic algorithm, improved GA, particle swarm optimization, Democratic PSO, and artificial bee colony are some other algorithms for better optimization with area coverage.
Voltage Regulation Planning Based on Optimal Grid-Connected Renewable Energy Allocation Using Nature-Inspired Algorithms to Reduce Switching Cycles of On-Load Tap Changing Transformer
Published in Electric Power Components and Systems, 2023
Hamid K. Ali, Ahmed M. A. Haidar, Norhuzaimin Julai, Andreas Helwig
Several optimization algorithms based on heuristic approaches can be used to solve the optimization problem given in (11). However, some of them may easily get stuck in the local minima such as the genetic algorithm. In this work, a cuckoo search optimizer is used and compared with a genetic optimizer. The cuckoo search algorithm is a novel meta-heuristic optimization method developed based on the concept of the bird family lifestyle known as cuckoos. The cuckoo is a fascinating bird with a beautiful voice and an aggressive reproduction strategy, particularly its egg-laying and breeding features, where generations of offspring are produced by employing Levy flights and random walks [3, 36]. CSA that uses a sort of elitism begins with the establishment of a base population. Cuckoos are classified into two distinct subpopulations: adult cuckoos and eggs [38]. Generally, the optimization technique relies on the cuckoos’ endurance struggle, which results in the death of some cuckoos or their eggs. As such, the remaining cuckoos relocate toward a more favorable area, reproduce, and lay eggs. Finally, the cuckoos’ survival effort converges on the establishment of a single cuckoo society with identical profit values.
Multi-objective optimisation of a grid-connected hybrid PV-battery system considering battery degradation
Published in International Journal of Sustainable Engineering, 2021
Vinay Anand Tikkiwal, Sajai Vir Singh, Hari Om Gupta
Cuckoo search is a meta-heuristic algorithm that draws inspiration from the breeding behaviour of cuckoos (Yang and Deb 2010). A cuckoo lays an egg (candidate solution) and drops it into other bird’s nest. Cuckoos adopt myriad strategies of the host birds’ infants to ensure the survival of their offspring. Cuckoo search algorithm combines this behaviour of cuckoos along with Lévy flights to generate new candidate solutions for replacement of lower quality solutions in the population. Lévy flights represents a random walk model, characterised by initial smaller random step sizes followed by larger ones (Zhang, Wang, and Wu 2012). The quality of any candidate solution is considered as proportional to the corresponding objective function value. At the end of each generation, a fraction, pa∈[0,1] of the population which essentially are the solutions having the lowest fitness values, are replaced with new solutions, randomly generated.
Surface altering optimisation in slope stability analysis with non-circular failure for random limit equilibrium method
Published in Georisk: Assessment and Management of Risk for Engineered Systems and Geohazards, 2021
Ramin Mafi, Sina Javankhoshdel, Brigid Cami, Reza Jamshidi Chenari, Amir H. Gandomi
Another rigorous nature-based global metaheuristic search method reported in the literature is the Cuckoo search method (Yang and Deb 2009) to find the critical non-circular slip surface. Cuckoo search is a global optimisation algorithm inspired by the natural parasitic but successful behaviour of the Cuckoo species by laying their eggs in the nests of other host birds (of other species). Cuckoo Search shows great promise, outperforming some more traditional global optimisation algorithms such as PSO and HS under standard test functions – taking fewer function evaluations to achieve the same level of solution accuracy (Wu 2012). This method is used in this study to find the global critical non-circular slip surface.