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
Dynamic scheduling of independent tasks in cloud computing applying a new hybrid metaheuristic algorithm including Gabor filter, opposition-based learning, multi-verse optimizer, and multi-tracker optimization algorithms
TL;DR: Simulation results applying NASA-iPSC real dataset showed that MTOA-GOMVO outweighs the baseline metaheuristic algorithms and performs well in scheduling cloud computing tasks.
7
Optimal image-based task-sequence/path planning and robust hybrid vision/force control of industrial robots
TL;DR: In this paper , an image-based task-sequence/path planning scheme coupled with a robust vision/force control method is suggested for solving the multi-task operation problem of an eye-in-hand (EIH) industrial robot interacting with a workpiece.
5
Hybrid continuous-discrete time control strategy to optimize thermal network dynamic storage and heat-electricity integrated energy system dynamic operation
Weijia Yang,Yuping Huang,Tong Ye,Feng Pan,Daiqing Zhao +4 more
TL;DR: A novel multi-layer control strategy optimizes heat-electricity integrated energy system operation by minimizing costs, utilizing thermal network dynamic storage, and enhancing renewable energy generation, achieving a 34.5% reduction in operating costs through continuous-time generalized predictive control.
2
A novel scenario generation and forecasting method for multiple future states at source side based on combinatorial models
Shunjiang Wang,Zihan Li,Kun Xu,Ximing Zhang,Jingbo Huang +4 more
1
Double-Objective Optimization Based on Movement Dynamics of Charged Particles
Vahab Nekoukar
- 01 Apr 2019
TL;DR: Results indicate not only the presented algorithm can find the Pareto solutions in the double-objective functions but also it performs better than other algorithms, generally.
1
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.