A novel phase angle-encoded fruit fly optimization algorithm with mutation adaptation mechanism applied to UAV path planning

Zhang, Xiangyin; Lu, Xingyang; Jia, Songmin; Li, Xiuzhi

doi:10.1016/j.asoc.2018.05.030

Cited by 77 publications

(30 citation statements)

References 31 publications

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“…FOA belongs to the domain of evolutionary computation; it realizes the optimization of complex problems by simulating fruit flies to search for food sources by using olfaction and vision. It has been successfully applied to the predictive control fields [30,31]. However, similar to those swarm intelligent optimization algorithms with iterative searching mechanisms, the standard FOA also has drawbacks such as a premature convergent tendency, a slow convergent rate in the later searching stage, and poor local search performance [32].…”

Section: Relevant Literature Reviewsmentioning

confidence: 99%

“…Compare whether the contemporary odor concentration, Best_Smell i=current , is better than the global optima, Best_Smell i . If so, update the global value by Equation 14, and enable the individual quantum fruit fly to fly to the optimal position with vision, as in Equations (29) and (30), then go to Step 6. Otherwise, go to Step 6 directly.…”

Section: Chaotic Quantum Global Perturbationmentioning

confidence: 99%

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Hybridizing Chaotic and Quantum Mechanisms and Fruit Fly Optimization Algorithm with Least Squares Support Vector Regression Model in Electric Load Forecasting

Geng

Hong

et al. 2018

Energies

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Compared with a large power grid, a microgrid electric load (MEL) has the characteristics of strong nonlinearity, multiple factors, and large fluctuation, which lead to it being difficult to receive more accurate forecasting performances. To solve the abovementioned characteristics of a MEL time series, the least squares support vector machine (LS-SVR) hybridizing with meta-heuristic algorithms is applied to simulate the nonlinear system of a MEL time series. As it is known that the fruit fly optimization algorithm (FOA) has several embedded drawbacks that lead to problems, this paper applies a quantum computing mechanism (QCM) to empower each fruit fly to possess quantum behavior during the searching processes, i.e., a QFOA algorithm. Eventually, the cat chaotic mapping function is introduced into the QFOA algorithm, namely CQFOA, to implement the chaotic global perturbation strategy to help fruit flies to escape from the local optima while the population’s diversity is poor. Finally, a new MEL forecasting method, namely the LS-SVR-CQFOA model, is established by hybridizing the LS-SVR model with CQFOA. The experimental results illustrate that, in three datasets, the proposed LS-SVR-CQFOA model is superior to other alternative models, including BPNN (back-propagation neural networks), LS-SVR-CQPSO (LS-SVR with chaotic quantum particle swarm optimization algorithm), LS-SVR-CQTS (LS-SVR with chaotic quantum tabu search algorithm), LS-SVR-CQGA (LS-SVR with chaotic quantum genetic algorithm), LS-SVR-CQBA (LS-SVR with chaotic quantum bat algorithm), LS-SVR-FOA, and LS-SVR-QFOA models, in terms of forecasting accuracy indexes. In addition, it passes the significance test at a 97.5% confidence level.

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Section: Relevant Literature Reviewsmentioning

confidence: 99%

Section: Chaotic Quantum Global Perturbationmentioning

confidence: 99%

Hybridizing Chaotic and Quantum Mechanisms and Fruit Fly Optimization Algorithm with Least Squares Support Vector Regression Model in Electric Load Forecasting

Geng

Hong

et al. 2018

Energies

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“…FFOA is applied in the UAV (unnamed aerial vehicles) path planning which simultaneously presents the improved version of the FFOA [8] 4.…”

Section: Resultsmentioning

confidence: 99%

Fruit Fly Algorithm: A Brief Review

Rautray

Biswas

Dash

et al. 2020

Smart Innovation, Systems and Technologies

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An optimization algorithm gives an optimum solution by executing iteratively and comparing it with different solutions. Therefore, many optimization algorithms have been discussed and used in all kinds of fields in the past few years. In this study, a brief description of an optimization algorithm called Fruit Fly algorithm (FFOA) is presented. This algorithm is based on the concept of the fruit flies and their extraordinary methods in finding a way to their food. Due to this, it has been applied in many of the research fields like science, engineering, medical, agriculture, etc., in many forms.

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“…Similar to the other swarm-intelligence algorithms, the conventional FOA still has the drawback that it easily falls into a local optimum. In order to improve the search efficiency and global search ability of the conventional FOA, Zhang et al proposed a novel angle-encoded FOA with mutation adaptation mechanism (θ-MAFOA) and adopted the novel algorithm to the UAV path planning problem [21]. With the mutation adaption mechanism, the ability of exploitation and exploration of FOA are balanced.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

Xia

Zhang

2018

Algorithms

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Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (QPIO) has been widely applied to such nonlinear problems. However, conventional QPIO is suffering low global convergence speed and local optimum. In order to solve the above problems, an improved QPIO algorithm, adaptive operator QPIO, is proposed in this paper. Firstly, a new initialization process based on logistic mapping method is introduced to generate the initial population of the pigeon-swarm. After that, to improve the performance of the map and compass operation, the factor parameter will be adaptively updated in each iteration, which can balance the ability between global and local search. In the final landmark operation, the gradual decreasing pigeon population-updating strategy is introduced to prevent premature convergence and local optimum. Finally, the demonstration of the proposed algorithm on UAV path planning problem is presented, and the comparison result indicates that the performance of our algorithm is better than that of particle swarm optimization (PSO), pigeon-inspired optimization (PIO), and its variants, in terms of convergence and accuracy.

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A novel phase angle-encoded fruit fly optimization algorithm with mutation adaptation mechanism applied to UAV path planning

Cited by 77 publications

References 31 publications

Hybridizing Chaotic and Quantum Mechanisms and Fruit Fly Optimization Algorithm with Least Squares Support Vector Regression Model in Electric Load Forecasting

Hybridizing Chaotic and Quantum Mechanisms and Fruit Fly Optimization Algorithm with Least Squares Support Vector Regression Model in Electric Load Forecasting

Fruit Fly Algorithm: A Brief Review

Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

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