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
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Mahmoud Elsisi,Mahmoud Elsisi +1 more
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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
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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
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References
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Tapabrata Ray,Pankaj Saini +1 more
TL;DR: A new swarm algorithm for single objective design optimization problems is presented that ensures that all the individuals in the swarm are unique as in a real swarm, where at a given time instant two individuals cannot share the same location.
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On Honey Bees and Dynamic Server Allocation in Internet Hosting Centers
Sunil Nakrani,Craig A. Tovey +1 more
TL;DR: A new decentralized honey bee algorithm which dynamically allocates servers to satisfy request loads, and is compared against an omniscient optimality algorithm, a conventional greedy algorithm, and an algorithm that computes omnisciently the optimal static allocation.
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•Journal Article
Solving Engineering Optimization Problems with the Simple Constrained Particle Swarm Optimizer
TL;DR: Solving Engineering Optimization Problems with the Simple Constrained Particle Swarm Optimizer.
Derivative-Free Filter Simulated Annealing Method for Constrained Continuous Global Optimization
TL;DR: The computational results obtained by the FSA method are promising and show a superior performance of the proposed method, which is a point-to-point method, against population-based methods.
A novel meta-heuristic optimization algorithm inspired by group hunting of animals: Hunting search
TL;DR: Several benchmark numerical optimization problems, constrained and unconstrained, are presented here to demonstrate the effectiveness and robustness of the proposed Hunting Search (HuS) algorithm and indicate that the proposed method is a powerful search and optimization technique.
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