A comprehensive overview on variants of CUCKOO search algorithm and applications

Rakesh, Spoorthi; Mahesh, Shanthi

doi:10.1109/iceeccot.2017.8284569

Cited by 9 publications

(4 citation statements)

References 5 publications

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“…Ref. [103] compiled and categorized 12 typical variants of the CS and their respective application domains from its inception to 2017. Ref.…”

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

Research Progress of Nature-Inspired Metaheuristic Algorithms in Mobile Robot Path Planning

Xu,

Li,

et al. 2023

Electronics

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The research of mobile robot path planning has shifted from the static environment to the dynamic environment, from the two-dimensional environment to the high-dimensional environment, and from the single-robot system to the multi-robot system. As the core technology for mobile robots to realize autonomous positioning and navigation, path-planning technology should plan collision-free and smooth paths for mobile robots in obstructed environments, which requires path-planning algorithms with a certain degree of intelligence. Metaheuristic algorithms are widely used in various optimization problems due to their algorithmic intelligence, and they have become the most effective algorithm to solve complex optimization problems in the field of mobile robot path planning. Based on a comprehensive analysis of existing path-planning algorithms, this paper proposes a new algorithm classification. Based on this classification, we focus on the firefly algorithm (FA) and the cuckoo search algorithm (CS), complemented by the dragonfly algorithm (DA), the whale optimization algorithm (WOA), and the sparrow search algorithm (SSA). During the analysis of the above algorithms, this paper summarizes the current research results of mobile robot path planning and proposes the future development trend of mobile robot path planning.

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“…Ref. [103] compiled and categorized 12 typical variants of the CS and their respective application domains from its inception to 2017. Ref.…”

Section: Cuckoo Search Algorithmmentioning

confidence: 99%

Research Progress of Nature-Inspired Metaheuristic Algorithms in Mobile Robot Path Planning

Xu,

Li,

et al. 2023

Electronics

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“…The study by Rakesh and Mahesh [71] describes the successful application of the nature-inspired cuckoo algorithm, with various variations, to comprehensive global optimization problems. CS has been shown to outperform other metaheuristics in terms of speed and accuracy in solving the traveling salesman problem (TSP) [72].…”

Section: Cuckoo Search Algorithm (Cs)mentioning

confidence: 99%

Integrating Flight Scheduling, Fleet Assignment, and Aircraft Routing Problems with Codesharing Agreements under Stochastic Environment

Kızıloğlu,

Sakallı

2023

Aerospace

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Airlines face the imperative of resource management to curtail costs, necessitating the solution of several optimization problems such as flight planning, fleet assignment, aircraft routing, and crew scheduling. These problems present some challenges. The first pertains to the common practice of addressing these problems independently, potentially leading to locally optimal outcomes due to their interconnected nature. The second challenge lies in the inherent uncertainty associated with parameters like demand and non-cruise time. On the other hand, airlines can employ a strategy known as codesharing, wherein they operate shared flights, in order to minimize these challenges. In this study, we introduce a novel mathematical model designed to optimize flight planning, fleet assignment, and aircraft routing decisions concurrently, while accommodating for codesharing. This model is formulated as a three-stage non-linear mixed-integer problem, with stochastic parameters representing the demand and non-cruise time. For smaller-scale problems, optimization software can effectively solve the model. However, as the number of flights increases, conventional software becomes inadequate. Moreover, considering a wide array of scenarios for stochastic parameters leads to more robust results; however, it is not enabled because of the limitations of optimization software. In this work, we introduce two new simulation-based metaheuristic algorithms for solving large-dimensional problems, collectively called “simheuristic.” These algorithms integrate the Monte Carlo simulation technique into Simulated Annealing and Cuckoo Search. We have applied these simheuristic algorithms to various problem samples of different flight sizes and scenarios. The results demonstrate the efficacy of our proposed modeling and solution approaches in efficiently addressing flight scheduling, fleet assignment, and aircraft routing problems within acceptable timeframes.

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“…A meta-heuristic approach based on the insect's casual nature like Cuckoo and Levy have been developed known as Cuckoo Search Algorithms (CSA), [31]. In [32] and [33] a detailed review of different modifications of Cuckoo algorithms discussed for the purpose of optimization and its significance in the research. to solve this complex nature of ELD a cuckoo algorithm is implemented [34].A variant of firefly algorithm (MFO) is implemented in [35] for optimization and a comparative analysis is made based on results with different algorithms used in a literature.…”

Section: Related Workmentioning

confidence: 99%

Economic Dispatch Solution for Generating Units through Optimization

Nazeer¹,

Jamil²,

Mohsin³

et al. 2020

IJEW

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The complex nature of the modern power system is because of the interconnection of the generating units and day by day increased load demand. The main purpose of the power system is to tantalize the load needs and generate reliable and cheap energy i.e. cost of generation should be optimum i.e. operating every generating unit in such a way to optimize the cost. The cost function of every generating unit is different from other generating unit, so load has to be divided among various generating units to obtain optimum generation. The optimization of power system is done by Economic load dispatch (ELD). ELD is of immense importance in power system operation and planning (PSOP). The primary purpose of ELD is to pacify the load needs at lowest cost while satiating all kind of equality and inequality constraints. Power system has highly non-linear input output characteristics because of different generation constraints. In ELD cost function of each and every generating unit is equated as quadratic function. Numerous methods have been devised to figure out ELD problem incorporating conventional methods like Lambda iteration method and Gradient method, and artificial intelligent method like Particle Swarm Optimization, Generic Algorithms etc. In this thesis optimization of generating units is done through General Algebraic Modelling System (GAMS). GAMS is a modelling system used for mathematical programming and optimization on a large scale. It helps to develop a mathematical model similar to their corresponding mathematical expression and gives more accurate results.

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A comprehensive overview on variants of CUCKOO search algorithm and applications

Cited by 9 publications

References 5 publications

Research Progress of Nature-Inspired Metaheuristic Algorithms in Mobile Robot Path Planning

Research Progress of Nature-Inspired Metaheuristic Algorithms in Mobile Robot Path Planning

Integrating Flight Scheduling, Fleet Assignment, and Aircraft Routing Problems with Codesharing Agreements under Stochastic Environment

Economic Dispatch Solution for Generating Units through Optimization

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