An Improved Quantum Evolutionary Algorithm Based on Artificial Bee Colony Optimization

Duan, Haibin; Zhang, Xing; Xu, Chunfang

doi:10.1007/978-3-642-03156-4_27

Cited by 13 publications

(5 citation statements)

References 4 publications

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“…Duan et al [37] proposed a hybrid method combining Quantum Evolutionary Algorithm (QEA) and ABC to overcome the limitations of stagnation. In their method, ABC is responsible for enhancing the local search capability as well as randomness of populations, which in turn will help QEA to avoid the local optimum.…”

Section: C) Hybrid Abc Variantsmentioning

confidence: 99%

Computing with the collective intelligence of honey bees – A survey

Anguluri

Lynn

Das

et al. 2017

Swarm and Evolutionary Computation

109

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Section: C) Hybrid Abc Variantsmentioning

confidence: 99%

Computing with the collective intelligence of honey bees – A survey

Anguluri

Lynn

Das

et al. 2017

Swarm and Evolutionary Computation

109

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“…[20] combined ABC with GA, and used crossover and exchange mechanism in GA to improve the update formula of bee, so that it can jump out of local optimal solution. The concept of QE-ABC is proposed to improve the randomness of initial population and individual local exploration ability in [21].…”

Section: Introductionmentioning

confidence: 99%

“…Despite these improved algorithms [18][19][20][21] have been proved to be effective in solving optimization problems, there are still the following shortcomings: The improved method is limited to partial optimization of the algorithm, and does not consider the characteristics of the bee popuplation as a whole. Although it produces better optimization results in individual stages, when the dimension of the optimization problem increases, the performance of the ABC algorithm will be reduced like other swarm intelligence algorithms.…”

Section: Introductionmentioning

confidence: 99%

Improved Artificial Bee Colony Algorithm Based on Multi-Strategy Synthesis for UAV Path Planning

Lin

et al. 2022

IEEE Access

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Path planning is the key for unmanned aerial vehicle (UAV) to perform tasks efficiently, which needs to quickly obtain the optimal path in the complex environment. To solve the path planning problem in the complex urban environment, an improved artificial bee colony algorithm based on multistrategy synthesis (IABC) is proposed to generate the appropriate path for the UAV. The IABC is based on the hybrid mechanism of chaotic mapping and Pareto principle to initialize the population, so as to fully search the solution space and provide an approximate optimal flight path for UAV. Meanwhile, in order to balance exploration and development, two new search equations are designed to generate candidate solutions, which provide more superior flight paths for UAVs. In addition, tangent random evolution mechanism is added to enhance the strategy of updating the algorithm offspring, maximize the quality of flight path generation, and effectively solve the path planning problem. Aiming at the complex urban environment and the multi-constraint optimization problem of UAV flight, the IABC is combined with cubic spline interpolation to generate a smooth path to meet the UAV flight maneuver characteristics. The improved algorithm proposed in this paper has been compared with Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Search Artificial Bee Colony (SABC) and Gbest Artificial Bee Colony (GABC). The algorithm proposed in this paper has good feasibility and effectiveness in solving the UAV path planning problem.INDEX TERMS artificial bee colony algorithm (ABC), path planning, unmanned aerial vehicle (UAV), cubic spline interpolation.

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“…However, it is easy to fall into a local optimal solution during the ABC algorithm iteration. To this end, many improved ABC algorithms have been proposed, such as Rosenbrocks rotational direction strategy [ 28 ], Boltzmann selection strategy [ 29 ], DE-ABC (differential evolution-ABC) [ 30 ], PSO-ABC (particle swarm optimization-ABC) [ 31 ], and QE-ABC (quantum evolutionary-ABC) [ 32 ]. Although those modified ABC algorithms have been widely used in various path planning problems, they were limited by the inherent limitations of the heuristics.…”

Section: Introductionmentioning

confidence: 99%

Path Planning of Unmanned Autonomous Helicopter Based on Human-Computer Hybrid Augmented Intelligence

et al. 2021

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Unmanned autonomous helicopter (UAH) path planning problem is an important component of the UAH mission planning system. The performance of the automatic path planner determines the quality of the UAH flight path. Aiming to produce a high-quality flight path, a path planning system is designed based on human-computer hybrid augmented intelligence framework for the UAH in this paper. Firstly, an improved artificial bee colony (I-ABC) algorithm is proposed based on the dynamic evaluation selection strategy and the complex optimization method. In the I-ABC algorithm, the following way of on-looker bees and the update strategy of nectar source are optimized to accelerate the convergence rate and retain the exploration ability of the population. In addition, a space clipping operation is proposed based on the attention mechanism for constructing a new spatial search area. The search time can be further reduced by the space clipping operation under the path planning result within acceptable changes. Moreover, the entire optimization process and results can be feeded back to the knowledge database by the human-computer hybrid augmented intelligence framework to guide subsequent path planning issues. Finally, the simulation results confirm that a feasible and effective flight path can be quickly generated by the UAH path planning system based on human-computer hybrid augmented intelligence.

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An Improved Quantum Evolutionary Algorithm Based on Artificial Bee Colony Optimization

Cited by 13 publications

References 4 publications

Computing with the collective intelligence of honey bees – A survey

Computing with the collective intelligence of honey bees – A survey

Improved Artificial Bee Colony Algorithm Based on Multi-Strategy Synthesis for UAV Path Planning

Path Planning of Unmanned Autonomous Helicopter Based on Human-Computer Hybrid Augmented Intelligence

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