A Cooperated fruit fly optimization algorithm (CFOA) is proposed for knapsack problems. In CFOA, a group generating strategy is designed for generating the initial solution. A novel cooperation strategy is used to enh...
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ISBN:
(纸本)9781538635247
A Cooperated fruit fly optimization algorithm (CFOA) is proposed for knapsack problems. In CFOA, a group generating strategy is designed for generating the initial solution. A novel cooperation strategy is used to enhance the connection and communication between flies. A repair operator based on value-weight ratio of each item is employed to guarantee the feasibility of the solution and enhance the usage rate of the constraint. Extensive numerical experiments are conducted on some well-known benchmark instances and the results show that CFOA presents extreme fast convergence speed and accuracy.
It is proposed that a hybrid algorithm to solve systems of non-linear equations based on fruit fly optimization algorithm (FOA) in the paper. In consideration of the imperfections that the low computation accuracy of ...
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ISBN:
(纸本)9781538621653
It is proposed that a hybrid algorithm to solve systems of non-linear equations based on fruit fly optimization algorithm (FOA) in the paper. In consideration of the imperfections that the low computation accuracy of the basic FOA algorithm and simplex method (SM) easily falls into local optimum, we present the simplex fruit fly optimization algorithm (SFOA) by combing FOA and SM, which has strong global and local searching abilities, in order to avoid the shortcomings existing in the basic algorithms. The results of calculation examples show that the SFOA algorithm can achieve a higher convergence accuracy and reliability, and therefore a brand new global solution strategy is provided to solve systems of non-linear equations.
The cracking of piezoelectric ceramic components is one of the main failure pattern of ultrasonic motors. The degradation characteristics can be extracted effectively by monitoring the voltage signal of piezoelectric ...
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ISBN:
(纸本)9781538621561
The cracking of piezoelectric ceramic components is one of the main failure pattern of ultrasonic motors. The degradation characteristics can be extracted effectively by monitoring the voltage signal of piezoelectric sensor. High dimensional feature vector contains a lot of redundant information due to dimension disaster. Locality preserving projection (LPP) can effectively reduce dimension on the degradation feature, which can fuse the high dimension feature. The parameters of support vector machine (SVM) have an important influence on the generalization ability of the model. fruit fly optimization algorithm (FOA) has the advantages of few parameters, fast calculation speed, strong ability of global optimization and easy to implement. FOA is utilized to optimize SVM in this paper accordingly, and the optimized SVM by FOA (FOASVM) is applied in degradation state recognition of ultrasonic motor. Finally, the effectiveness of the proposed method is verified through the comparative analysis.
This paper presents a new Modified fruit fly optimization algorithm (MFOA) which is used to find the optimal PID controllers parameters applied to control a two-link robotic manipulator. The proposed new distribution ...
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ISBN:
(纸本)9781538621349
This paper presents a new Modified fruit fly optimization algorithm (MFOA) which is used to find the optimal PID controllers parameters applied to control a two-link robotic manipulator. The proposed new distribution law in MFOA for some of the fruit flies improves searching diversity in earlier iterations and increases solution precession in last iterations. In order to apply the PID controllers to the robot manipulator, a nonlinear feedback linearization control technique is employed which can fully linearize and decouple nonlinear robot's dynamics. Simulation results confirm that the MFOA-PID controller can achieve better closed-loop system responses with respect to the original FOA-PID controller.
To overcome the disadvantages of slow convergence speed, easily relapsing into local extremum and poor stability of traditional fruit fly optimization algorithm (FOA), an improved FOA incorporating Average Learning an...
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ISBN:
(纸本)9781538635735
To overcome the disadvantages of slow convergence speed, easily relapsing into local extremum and poor stability of traditional fruit fly optimization algorithm (FOA), an improved FOA incorporating Average Learning and Step Changing into the evolutionary process (AL-SC-FOA) is proposed. The combination of strategies of average learning and step changing can balance the ability of global search and local search of the algorithm, accelerate the convergence speed, improve the accuracy and enhance the stability of the algorithm. The proposed AL-SC-FOA is applied to six standard examples of the optimization test. The experimental results show that the AL-SC-FOA can avoid falling into the local optimum, which has higher precision and faster convergence speed, as well as better stability.
Two dimensional strip-packing problem (2DSPP) consists of packing a set of rectangular items on one strip with a restriction of a maximal width and height. Because the conventional algorithms are still sub-optimal, th...
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Two dimensional strip-packing problem (2DSPP) consists of packing a set of rectangular items on one strip with a restriction of a maximal width and height. Because the conventional algorithms are still sub-optimal, the researchers tend towards searching for more successful alternative algorithms to solve 2DSPP. The fruit fly optimization algorithm (FOA), which is one of the recently proposed meta-heuristic algorithms, has been successfully applied on many engineering and mathematical problems. This study presents an implementation of FOA for solving non-oriented 2DSPP. The aim of the study is to find the optimal sequence of the rectangles in a strip, and then to place the rectangles by bottom left fill approach to have the optimal height within a fixed width box. The experiments are concluded on online available set of 2DSPP test problems. The preliminary results of the study are compared with the results of some conventional or heuristic approaches which use the same problem set. The experimental results show the promising results are obtained by FOA on solving 2DSPPs. (c) 2017 The Authors. Published by Elsevier B.V.
fruit fly optimization algorithm(FOA) is inspired by imitating the foraging activity of fruit flies. Aiming at its inability to search the entire solution space, a Self-Adaptive Modified fruitflyoptimization Algorit...
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ISBN:
(纸本)9781538629185
fruit fly optimization algorithm(FOA) is inspired by imitating the foraging activity of fruit flies. Aiming at its inability to search the entire solution space, a Self-Adaptive Modified fruit fly optimization algorithm(SAMFOA) is proposed. Firstly, a new calculation formula of the smell concentration judgment value is designed. With the use of the new formula, the smell concentration judgment value is no longer restricted to be non-negative value so the algorithm is able to search both the positive and negative part of the solution space. Secondly, a self-adaptive osphresis foraging radius is introduced to enhance the ability to break away from local optimum. Experiments on 20 numerical benchmark functions show that the algorithm has good performance in terms of global searching ability, optimize accuracy and stability.
In this study, an application of fruit fly optimization algorithm (FOA) is presented. FOA is one of the recently proposed swarm intelligence optimizationalgorithms used to solve continuous complex optimization proble...
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In this study, an application of fruit fly optimization algorithm (FOA) is presented. FOA is one of the recently proposed swarm intelligence optimizationalgorithms used to solve continuous complex optimization problems. FOA has been invented by Pan in 2011 and it is based on the food search behavior of fruit flies. The FOA has a simple framework and it is easy to implement for solving optimization problem with different characteristics. The FOA is also a robust and fast algorithm and some researchers used FOA to solve discrete optimization problems. In this study, a new modified FOA is proposed for solving the well-known traveling salesman problem (TSP) which is one of the most studied discrete optimization problems. In basic FOA, there are two basic phases, one of them is osphresis phase and the other is vision phase. In the modified version of FOA the ospherisis phases kept as it is and for vision phase two different methods developed. In vision phase, the first half of the city arrangement matrix is updated according to first %30 part of best solutions of the ospheresis phase. The other half of the city arrangement matrix is randomly reproduced because of the possibility that initial solutions are far from the optimum. According to the results, travelling salesman problem can be solved with FOA as an alternative method. For big scale problems, it needs some improvements. (c) 2017 The Authors. Published by Elsevier B.V.
In this paper, a discrete fruit fly optimization algorithm（DFOA） is proposed for solving the capacitated vehicle routing problem（CVRP）. Firstly, a two-part discrete array is presented to represent the solution. Se...
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ISBN:
(纸本)9781538629185
In this paper, a discrete fruit fly optimization algorithm（DFOA） is proposed for solving the capacitated vehicle routing problem（CVRP）. Firstly, a two-part discrete array is presented to represent the solution. Secondly, the initialization based on K-means is proposed to take full use of the location information of the customers. The customers in each cluster are allocated to one vehicle. Meanwhile, the repair operation is designed for not taking the capacity constraint into account in the initialization. Due to the characteristics of the CVRP, it may be effective to exchange or reallocate the customers between the neighbor routes in the graph. Thus, the smell-based search and vision-based search with the specific problem feature are designed. Moreover, the elimination mechanism and the simulated annealing based search are used to balance the exploration and the exploitation capabilities. In addition, the effect of parameter setting is investigated by using the Taguchi method of design-of-experiment to obtain the suitable values. Finally, numerical tests with the benchmark instances are carried out, which demonstrate the effectiveness of the proposed algorithm.
In order to solve the shortcomings of fruit fly optimization algorithm(FOA),which is slow and easy to fall into local optimum,can not specify the domain,fruit fly optimization algorithm and logstic function transforma...
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ISBN:
(纸本)9780995886766
In order to solve the shortcomings of fruit fly optimization algorithm(FOA),which is slow and easy to fall into local optimum,can not specify the domain,fruit fly optimization algorithm and logstic function transformation are combined to propose an adaptive step improved fruit fly optimization algorithm with logistic transform(ASFOALT). The algorithm improves the correctness of the optimal solution range by improving the fitness function of the FOA,and improves the overall performance of the algorithm by adding the adaptive step mechanism. The experimental results show that ASFOALT has a large improvement in global search capability,convergence speed,convergence accuracy and reliability.
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