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Quantum Behavior-Based Enhanced Fruit Fly optimization Algorithm with Application to UAV Path Planning

INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2020年第1期13卷 1315-1331页

作者： Zhang, Xiangyin Xia, Shuang Li, Xiuzhi Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Minist Educ Engn Res Ctr Digital Community Beijing 100124 Peoples R China Beijing Lab Urban Mass Transit Beijing 100124 Peoples R China Beijing Key Lab Computat Intelligence & Intellige Beijing 100124 Peoples R China

As a newly developed simple and effective optimization technology, the fruit fly optimization algorithm (FOA) has been successfully applied in many fields. To accelerate the algorithm convergence and avoid the local optimum, the enhanced FOA based on quantum theory called QFOA is proposed in this paper. When establishing the quantum Delta potential well around the location of fruit fly swarm, QFOA introduces the quantum behavior-based searching mechanism into the original osphresis-based search procedure of FOA. In the process that fruit flies find and move toward the food source, fruit flies follow the wave function property of the Delta potential well rather than the Newtonian mechanics. Taking advantage of the probability and uncertainty of quantum theory, the proposed QFOA can effectively overcome the weakness in premature convergence and easy trapping into local optimum. Since there are two popular models of the basic FOA, this paper also develops two corresponding QFOAs. Experimental results on various benchmark functions show that both the two QFOA models has overall better performance compared with the basic FOA as well as other FOA variants and other well-known optimization algorithms. In addition, the proposed QFOAs are also applied to unmanned aerial vehicle (UAV) path planning problem in the three-dimensional environment, and comparative results about the obtained optimal flight path and population convergence process show the effectiveness of QFOAs. (C) 2020 The Authors. Published by Atlantis Press B.V.

关键词： Fruit fly optimization algorithm continuous function optimization Delta potential well Quantum behavior Path planning

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Use of Improved Gravitational Search Algorithm for 3D Reconstruction of Space Curves Using NURBS 7th

Use of Improved Gravitational Search Algorithm for 3D Recons...

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International Conference on Soft Computing for Problem Solving (SocProS)

作者： Singh, Amarjeet Deep, Kusum Univ Delhi Janki Devi Mem Coll Dept Math New Delhi 110060 India Indian Inst Technol Roorkee Dept Math Roorkee 247667 Uttrakhand India

ISBN: (纸本)9789811315923;9789811315916

Gravitational Search Algorithm (GSA) is a memory-less, nature-inspired algorithm for nonlinear continuous optimization problems. In Singh et al. (a new Improved Gravitational Search Algorithm for function optimization using a novel "best-so-far" update mechanism. IEEE, pp. 35-39 (2015) [21]), Singh and Deep proposed an Improved GSA using best-so-far mechanism. In this paper, the problem of 3D reconstruction is modelled as a nonlinear optimization problem. GSA and Improved GSA are used to solve three reconstruction problems. Based on the several computational experiments and analysis, it is concluded that the performance of improved GSA is better than original GSA in terms of convergence and solution quality.

关键词： Gravitational search algorithm continuous function optimization Heuristic technique Reconstruction NURBS

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Theoretical Analysis and Experimental Evidence of Opposite-Center Learning

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IEEE ACCESS 2018年 6卷 34948-34966页

作者： Xu, Qingzheng Xu, Hongpei Wu, Wei Xijing Univ Sch Informat Engn Xian 710123 Shaanxi Peoples R China Univ Amsterdam Fac Sci NL-1000 GG Amsterdam Netherlands Xian Univ Technol Sch Mech & Precis Instrument Engn Xian 710048 Shaanxi Peoples R China

The main motivation of this paper is to discuss some theoretical details of opposite-center learning (OCL) and further validate its effectiveness for the optimization problems. In order to reveal the strong flexibility of its definition, two analytical solutions of opposite-center point are deduced for 1-D case. In order to reduce its computational complexity for higher dimensions, several termination criterion of iterative process are discussed thoughtfully and then a simple and efficient criterion is found when considering both the algorithm performance and computation cost. Moreover, a uniform evaluation approach to compute an evaluation function is proposed and then different opposition strategies can be compared easily by means of the mathematical expectation of these functions. To further verify its practical performance, OCL mechanism is embedded into differential evolution (DE) for population initialization and generation jumping and opposite-center DE is proposed. Simulation results demonstrate the strong exploitation ability of OCL. The obtained results also confirm a good tradeoff of solution accuracy and convergence speed in solving various function optimizations.

关键词： Opposite-center learning opposition-based learning differential evolution sampling continuous function optimization

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An Adaptive Population-based Simplex Method for continuous optimization

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INTERNATIONAL JOURNAL OF SWARM INTELLIGENCE RESEARCH 2016年第4期7卷 23-51页

作者： Omran, Mahamed G. H. Clerc, Maurice Gulf Univ Sci & Technol Dept Comp Sci Hawally Kuwait

This paper proposes a new population-based simplex method for continuous function optimization. The proposed method, called Adaptive Population-based Simplex (APS), is inspired by the Low-Dimensional Simplex Evolution (LDSE) method. LDSE is a recent optimization method, which uses the reflection and contraction steps of the Nelder-Mead Simplex method. Like LDSE, APS uses a population from which different simplexes are selected. In addition, a local search is performed using a hyper-sphere generated around the best individual in a simplex. APS is a tuning-free approach, it is easy to code and easy to understand. APS is compared with five state-of-the-art approaches on 23 functions where five of them are quasi-real-world problems. The experimental results show that APS generally performs better than the other methods on the test functions. In addition, a scalability study has been conducted and the results show that APS can work well with relatively high-dimensional problems.

关键词： continuous function optimization Low-dimensional Simplex Evolution Nelder-Mead Simplex Population-based optimization Methods Triangle Evolution

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Clustering Search and Variable Mesh Algorithms for continuous optimization

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EXPERT SYSTEMS WITH APPLICATIONS 2015年第2期42卷 789-795页

作者： Costa Salas, Yasel J. Martinez Perez, Carlos A. Bello, Rafael Oliveira, Alexandre C. Chaves, Antonio A. Lorena, Luiz A. Univ Manizales Caldas Colombia Univ Cent Marta Abreu Las Villas Santa Clara Cuba Univ Fed Maranhao Sao Luis MA Brazil Univ Fed Sao Paulo Sao Paulo Brazil Inst Nacl Pesquisas Espaciais Sao Jose Dos Campos Brazil

The hybridization of population-based meta-heuristics and local search strategies is an effective algorithmic proposal for solving complex continuous optimization problems. Such hybridization becomes much more effective when the local search heuristics are applied in the most promising areas of the solution space. This paper presents a hybrid method based on Clustering Search (CS) to solve continuous optimization problems. The CS divides the search space in clusters, which are composed of solutions generated by a population meta-heuristic, called Variable Mesh optimization. Each cluster is explored further with local search procedures. Computational results considering a benchmark of multimodal continuous functions are presented. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： continuous function optimization Hybrid methods

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A Lyapunov-Based Extension to Particle Swarm Dynamics for continuous function optimization

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SENSORS 2009年第12期9卷 9977-9997页

作者： Bhattacharya, Sayantani Konar, Amit Das, Swagatam Han, Sang Yong Chung Ang Univ Sch Engn & Comp Sci Seoul 156756 South Korea Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 India

The paper proposes three alternative extensions to the classical global-best particle swarm optimization dynamics, and compares their relative performance with the standard particle swarm algorithm. The first extension, which readily follows from the well-known Lyapunov's stability theorem, provides a mathematical basis of the particle dynamics with a guaranteed convergence at an optimum. The inclusion of local and global attractors to this dynamics leads to faster convergence speed and better accuracy than the classical one. The second extension augments the velocity adaptation equation by a negative randomly weighted positional term of individual particle, while the third extension considers the negative positional term in place of the inertial term. Computer simulations further reveal that the last two extensions outperform both the classical and the first extension in terms of convergence speed and accuracy.

关键词： particle swarm dynamics metaheuristics continuous function optimization stability convergence lyapunov stability theorem

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Phase Angle-Encoded and Quantum-Behaved Particle Swarm optimization Applied to Three-Dimensional Route Planning for UAV

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IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART A-SYSTEMS AND HUMANS 2012年第2期42卷 511-526页

作者： Fu, Yangguang Ding, Mingyue Zhou, Chengping Huazhong Univ Sci & Technol Inst Pattern Recognit & Artificial Intelligence State Key Lab Multispectral Informat Proc Technol Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Coll Life Sci & Technol Educ Minist China Image Informat Proc & Intelligence Control Key La Wuhan 430074 Peoples R China

A new variant of particle swarm optimization (PSO), named phase angle-encoded and quantum-behaved particle swarm optimization (theta-QPSO), is proposed. Six versions of theta-QPSO using different mappings are presented and compared through their application to solve continuous function optimization problems. Several representative benchmark functions are selected as testing functions. The real-valued genetic algorithm (GA), differential evolution (DE), standard particle swarm optimization (PSO), phase angle-encoded particle swarm optimization (theta-PSO), quantum-behaved particle swarm optimization (QPSO), and theta-QPSO are tested and compared with each other on the selected unimodal and multimodal functions. To corroborate the results obtained on the benchmark functions, a new route planner for unmanned aerial vehicle (UAV) is designed to generate a safe and flyable path in the presence of different threat environments based on the theta-QPSO algorithm. The PSO,theta-PSO, and QPSO are presented and compared with the theta-QPSO algorithm as well as GA and DE through the UAV path planning application. Each particle in swarm represents a potential path in search space. To prune the search space, constraints are incorporated into the pre-specified cost function, which is used to evaluate whether a particle is good or not. Experimental results demonstrated good performance of the theta-QPSO in planning a safe and flyable path for UAV when compared with the GA, DE, and three other PSO-based algorithms.

关键词： continuous function optimization phase angle-encoded and quantum-behaved particle swarm optimization(theta-QPSO) route planning unmanned aerial vehicle (UAV)

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Fast Evolutionary Solution Finding for optimization Using Opposite Gradient Movement

Fast Evolutionary Solution Finding for Optimization Using Op...

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2011 Seventh International Conference on Natural Computation(第七届自然计算国际会议 ICNC 2011)

作者： Thirachit Saenphon Chidchanok Lursinsap Advanced Virtual and Intelligent (AVIC) Center Department of Mathematics Faculty of Science Chulalongkorn University Bangkok Thailand

In this paper, a hybrid algorithm of gradient movement is proposed. On a surface of continuous function, every random point has a gradient value of the function that minimize and convergence to zero when it is a neighborhood with the optimum solution. Each iteration calculates the gradient of function at every point and chooses a minimum gradient point with a shortest distance from the optimum solution to find a new closer candidate to be an optimum point. The comparative experiments were made between CA_PSO, PSO, CACO, and SGA. Results show the proposed algorithm with gradient movement techniques outperforms other.

关键词： Swarm intelligence Ant colony optimization (ACO) Particle swarm optimization (PSO) Gradient continuous function optimization

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StPSO: Strengthened particle swarm optimization

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2010年第6期18卷 1095-1114页

作者： Sevkli, Aise Zulal Sevilgen, Fatih Erdogan Fatih Univ Fac Engn Dept Comp Engn TR-34500 Istanbul Turkey Gebze Inst Technol Fac Engn Dept Comp Engn TR-41400 Gebze Kocaeli Turkey

In this paper, we present a novel approach to strengthen Particle Swarm optimization (PSO). PSO is a population-based metaheuristic that takes advantage of individual memory and social cooperation in a swarm. It has been applied to a variety of optimization problems because of its simplicity and fast convergence. However, straightforward application of PSO suffers from premature convergence and lack of intensification around the local best locations. To rectify these problems, we modify update procedure for the best particle in the swarm and propose a simple and random moving strategy. We perform a Reduced Variable Neighborhood Search (RVNS) based local search around the particle, as well. The resulting strengthened PSO (StPSO) algorithm not only has superior exploration and exploitation mechanisms but also provides a dynamical balance between them. Experimental analysis of StPSO is performed on continuous function optimization problems and a discrete problem, Orienteering Problem. Its performance is quite robust and consistent for all problem types;discrete or continuous, unimodal or multimodal. StPSO either reproduces the best known solution or provides a competitive solution for each problem instance. So, it is a valuable tool producing promising solutions for all problem types.

关键词： Particle Swarm optimization Reduced Variable Neighborhood Search continuous function optimization Orienteering Problem premature convergence local search

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Further example study on ant system algorithm based continuous space optimization

Further example study on ant system algorithm based continuo...

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4th World Congress on Intelligent Control and Automation

作者： Wang, L Wu, QD Tongji Univ Informat & Control Dept Shanghai 200092 Peoples R China

ISBN: (纸本)0780372689

In this paper, ant system (AS) optimization algorithm in continuous space[14,15] is under further study and used for other examples of optimum value searching of multi-minimum continuous function and another linear continuous function. The multi-minimum function of Rosenbrock function is chosen. The algorithm used is defined in detail in paper [14] and. [15]. In this paper, the general optimization error function for algorithm evaluation is modified, and the detail is given in another paper of the authors in WCICA'02 conference [16]. The applicability characters of AS application in continuous space optimization problems sire summarized at the end of this paper. The authors of this paper firmly believe that this paper will have reference meaning to intelligent theory and control application research area.

关键词： Ant System Algorithm continuous function optimization multi-minimum function

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