Journal Article10.1007/S40997-016-0066-9
Multi-tracker Optimization Algorithm: A General Algorithm for Solving Engineering Optimization Problems
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TL;DR: This new algorithm, which is named as “multi-tracker optimization algorithm,” due to a multi-level structure of trackers within it, has some unique features, such as increasing the accuracy of the optimal point and continuous local search after convergence in order to escape from local minima simultaneously.
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Abstract: In this paper, a new computational population-based optimization algorithm, which is designed based on the advantages and disadvantages of other evolutionary optimization algorithms introduced so far, is proposed. This new algorithm, which is named as “multi-tracker optimization algorithm,” due to a multi-level structure of trackers within it, has some unique features, such as increasing the accuracy of the optimal point and continuous local search after convergence in order to escape from local minima simultaneously. Another important advantage of this algorithm is optimizing time-varying dynamical problems and tracking the optimal point. These characteristics make the algorithm very efficient for optimization problems, especially in the field of engineering. For a thorough investigation and comparison of this algorithm with other efficient optimization algorithms, different optimization problems such as static, dynamic, unconstrained and constrained, each of which has different challenges, are considered. The results of applying this algorithm on the abovementioned basic problems show the superiority of this algorithm over other efficient evolutionary algorithms.
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Citations
SSC: A hybrid nature-inspired meta-heuristic optimization algorithm for engineering applications
TL;DR: Experimental results based on IEEE CEC’17 and six real-life engineering problems demonstrate the robustness, effectiveness, efficiency, and convergence analysis of the proposed SSC algorithm in comparison with other competitor approaches.
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Predicting Compressive Strength of Manufactured-Sand Concrete Using Conventional and Metaheuristic-Tuned Artificial Neural Network
TL;DR: Wang et al. as mentioned in this paper implemented two ANN-based scenarios to approximate the uniaxial compressive strength of manufactured-sand concrete, and two improved ANNs were created with metaheuristic algorithms, namely biogeography-based optimization (BBO) and multi-tracker optimization algorithm (MTOA).
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Optimal design of nonlinear model predictive controller based on new modified multitracker optimization algorithm
Mahmoud Elsisi,Mahmoud Elsisi +1 more
TL;DR: An optimal design for the nonlinear model predictive control (NLMPC) based on a new improved intelligent technique and it is named modified multitracker optimization algorithm (MMTOA), which improves the exploration behavior of the MTOA to prevent it from becoming trapped in a local optimum.
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SChoA: a newly fusion of sine and cosine with chimp optimization algorithm for HLS of datapaths in digital filters and engineering applications
TL;DR: The sine–cosine functions have been applied to update the equations of chimps during the search process for reducing the several drawbacks of the ChoA algorithm such as slow convergence rate, locating local minima rather than global minima, and low balance amid exploitation and exploration.
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Optimal interval type-2 fuzzy fractional order super twisting algorithm: A second order sliding mode controller for fully-actuated and under-actuated nonlinear systems
TL;DR: The simulation and experimental results demonstrate the superiority of the IT2FFOSTA in reducing the amount of chattering, tracking error, and control effort compared to those of the other control methods.
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References
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Path planning for unmanned underwater vehicle in 3d space with obstacles using spline-imperialist competitive algorithm and optimal interval type-2 fuzzy logic controller
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Sliding mode control of 3-rpr parallel robot on the optimal path using cuckoo optimization algorithm
Moezi Seyed Alireza,Rafeeyan Mansour,Ebrahimi Saeed +2 more
- 10 Apr 2015
TL;DR: It is concluded that based on the sliding mode control technique, the robot can follow the desired trajectory very precisely in spite of the presence of the uncertainties in length and mass of the robot's links.