Introduction to Computational Intelligence

Xing, Bo; Gao, Wenjing

doi:10.1007/978-3-319-03404-1_1

Cited by 10 publications

(9 citation statements)

References 122 publications

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“…Finally., it flies towards the food direction. 47,48 The standard FFO has been explained in details in the literature. 47–49 and some improvements and enhancements have been added to it in this work as follows,a) Assign a range of values as an upper and lower limits for the initialization of swarm location for the decision variables.b) A dynamic change of the search radius with some iteration has been proposed for the standard FFO algorithm to improve its performance and exclude the drawbacks of the fixed value searching radius (see Figure 6).…”

Section: Swarm-based Nnfopid Controller For Trajectory Tracking Of Ddmrmentioning

confidence: 99%

Trajectory tracking of differential drive mobile robots using fractional-order proportional-integral-derivative controller design tuned by an enhanced fruit fly optimization

Abed

Rashid

Abedi

et al. 2022

Measurement and Control

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This work proposes a new kind of trajectory tracking controller for the differential drive mobile robot (DDMR), namely, the nonlinear neural network fractional-order proportional integral derivative (NNFOPID) controller. The suggested controller’s coefficients comprise integral, proportional, and derivative gains as well as derivative and integral powers. The adjustment of these coefficients turns the design of the proposed NNFOPID control further problematic than the conventional proportional-integral-derivative control. To handle this issue, an Enhanced Fruit Fly Swarm Optimization algorithm has been developed and proposed in this work to tune the NNFOPID’s parameters. The enhancement achieved on the standard fruit fly optimization technique lies in the increased uncertainty in the values of the initialized coefficients to convey a broader search space. subsequently, the search range is varied throughout the updating stage by beginning with a big radius and declines gradually during the course of the searching stage. The proposed NNFOPID controller has been validated its ability to track specific three types of continuous trajectories (circle, line, and lemniscate) while minimizing the mean square error and the control energy. Demonstrations have been run under MATLAB environment and revealed the practicality of the designed NNFOPID motion controller, where its performance has been compared with that of a nonlinear Neural Network Proportional Integral Derivative controller on the tracking of one of the aforementioned trajectories of the DDMR.

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Section: Swarm-based Nnfopid Controller For Trajectory Tracking Of Ddmrmentioning

confidence: 99%

Trajectory tracking of differential drive mobile robots using fractional-order proportional-integral-derivative controller design tuned by an enhanced fruit fly optimization

Abed

Rashid

Abedi

et al. 2022

Measurement and Control

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“…Living organisms apply strategies to build social structures became the foundations of many algorithms as mentioned in the previous section. A comprehensive review of swarm intelligence methods can be found in [ 9 , 28 ].…”

Section: Swarm Intelligence Algorithms and Their Applicationsmentioning

confidence: 99%

“…That procedure allows them to avoid being stuck in a local optimum. Exact information regarding that algorithm can be found in [ 28 ] (pp. 232–233) and [ 29 , 30 ].…”

Section: Swarm Intelligence Algorithms and Their Applicationsmentioning

confidence: 99%

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A Swarm-Based Approach to Generate Challenging Mazes

Kwiecień

2018

Entropy

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Swarm intelligence draws its inspiration from the collective behaviour of many individual agents interacting with both one another and their environment. This paper presents a possibility to apply a swarm-based algorithm, modelled after the behaviour of individuals operating within a group where individuals move around in the manner intended to avoid mutual collisions, to create the most challenging maze developed on a board with determined dimensions. When solving such a problem, two complexity measures are used. Firstly, the complexity of the path was assumed to be a quality criterion, depending on the number of bends and the length of the path between two set points that was subjected to maximisation. Secondly, we focus on the well-known concept of the maze complexity given as the total complexity of the path and all branches. Owing to the uniqueness of the problem, consisting in the maze modification, a methodology was developed to make it possible for the individuals belonging to their population to make various types of movements, e.g., approach the best individual, within the range of visibility, or relocate randomly. The test results presented here indicate a potential prospect of application of the swarm-based methods to generate more and more challenging two-dimensional mazes.

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“…It can be seen from the above that traditional gradient-based mathematical programming algorithms are more readily replaced by novel and efficient heuristic methods inspired by biological, chemical or physical phenomena. Heuristic optimization techniques are gaining popularity among researchers, see, for example, Reference [29], because they are easy for numerical implementation, do not require gradient information and one can easily combine this type of algorithm with any finite element structural analysis code. This practical aspect of engineering the implementation of topology optimization techniques seems to be one of the most important issues for contemporary design.…”

Section: Introductionmentioning

confidence: 99%

GHOST—Gate to Hybrid Optimization of Structural Topologies

Bochenek

Tajs-Zielińska

2019

Materials

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Although well-recognized in the fields of structural and material design and widely present in engineering literature, topology optimization still arouses a high interest within research communities. Moreover, it is observed that the development of innovative, efficient and versatile methods is one of the most important issues stimulating progress within the topology optimization area. Following this activity, in the present study, a concept of a hybrid algorithm developed in order to generate optimal structural topologies of minimal compliance is presented. The hybrid algorithm is built based on two existing approaches. The first one makes use of the formal optimality criterion, whereas the second one utilizes a special heuristic rule of design variables updating. The main idea that stands behind the concept of the present proposal is to take the advantage of both algorithms capabilities. In a numerical implementation of the hybrid algorithm, the design variables are updated at each iteration step using both approaches, and the solution with a lower objective function value is selected for the next iteration. The numerical tests of the generation of minimal compliance structures have been performed for chosen structures including a real engineering one. It has been confirmed that the proposed hybrid technique based on switching between the considered rules allows the final structures having lower values of compliance as compared with the results of an application of basic algorithms running separately to be obtained. Moreover, based on promising results of the tests performed, one can consider the proposed concept of a hybrid algorithm as an alternative for other existing topology generators.

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Introduction to Computational Intelligence

Cited by 10 publications

References 122 publications

Trajectory tracking of differential drive mobile robots using fractional-order proportional-integral-derivative controller design tuned by an enhanced fruit fly optimization

Trajectory tracking of differential drive mobile robots using fractional-order proportional-integral-derivative controller design tuned by an enhanced fruit fly optimization

A Swarm-Based Approach to Generate Challenging Mazes

GHOST—Gate to Hybrid Optimization of Structural Topologies

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