Journal Article10.1007/S40997-016-0066-9
Multi-tracker Optimization Algorithm: A General Algorithm for Solving Engineering Optimization Problems
39
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.
read more
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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A combined framework based on data preprocessing, neural networks and multi-tracker optimizer for wind speed prediction
TL;DR: A novel combined forecasting framework that simultaneously considers data preprocessing, combined forecasting, and comprehensive evaluation is presented to address the drawbacks of existing methods.
59
Optimal adaptive interval type-2 fuzzy fractional-order backstepping sliding mode control method for some classes of nonlinear systems.
TL;DR: The AIT2FFOBSMC method exploits the advantages of backstepping and sliding mode methods to improve the performance of closed-loop control systems by lowering the tracking error and increasing robustness.
43
Fractional-order model and experimental verification for broadband hysteresis in piezoelectric actuators
TL;DR: In this paper, a fractional-order model was proposed to characterize the hysteresis of piezoelectric actuators in time and frequency domains, and the results showed that the identified model in frequency domain is preferable in a wider frequency range from 1 to 200 Hz, and maximum error is about 4.47%.
41
Novel Dynamic-Sliding-Mode-Manifold-Based Continuous Fractional-Order Nonsingular Terminal Sliding Mode Control for a Class of Second-Order Nonlinear Systems
TL;DR: Simulation results on SISO and MIMO nonlinear systems show that the proposed method has a better tracking performance than the general fractional-order nonsingular terminal sliding mode control and the stability and finite-time convergence of the closed-loop system are proven by the Lyapunov stability theory.
An efficient orthogonal opposition-based learning slime mould algorithm for maximum power point tracking
Essam H. Houssein,Bahaa El-din Helmy,Hegazy Rezk,Ahmed M. Nassef +3 more
- 09 Jan 2022
TL;DR: A new version of the SMA is introduced called mSMA-based on the hybridization of the original SMA with a modified versions of the opposition-based learning (mOBL) and the Orthogonal learning (OL) strategies to increase the dynamic response and to remove the oscillations that occurred at the steady-state response.
33
References
Optimization by Simulated Annealing
TL;DR: There is a deep and useful connection between statistical mechanics and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters), and a detailed analogy with annealing in solids provides a framework for optimization of very large and complex systems.
46.9K
Particle swarm optimization
James Kennedy,Russell C. Eberhart +1 more
- 06 Aug 2002
TL;DR: A concept for the optimization of nonlinear functions using particle swarm methodology is introduced, and the evolution of several paradigms is outlined, and an implementation of one of the paradigm is discussed.
44.1K
Differential Evolution – A Simple and Efficient Heuristic for Global Optimization over Continuous Spaces
Rainer Storn,Kenneth Price +1 more
TL;DR: In this article, a new heuristic approach for minimizing possibly nonlinear and non-differentiable continuous space functions is presented, which requires few control variables, is robust, easy to use, and lends itself very well to parallel computation.
A new optimizer using particle swarm theory
Russell C. Eberhart,James Kennedy +1 more
- 04 Oct 1995
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
16.4K
Ant system: optimization by a colony of cooperating agents
Marco Dorigo,Vittorio Maniezzo,Alberto Colorni +2 more
- 01 Feb 1996
TL;DR: It is shown how the ant system (AS) can be applied to other optimization problems like the asymmetric traveling salesman, the quadratic assignment and the job-shop scheduling, and the salient characteristics-global data structure revision, distributed communication and probabilistic transitions of the AS.