scholarly journals Composite Differential Search Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-15 ◽  
Author(s):  
Bo Liu
Keyword(s):  
Convergence Rate ◽  
Optimization Problem ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Control Parameters ◽  
Original Algorithm ◽  
New Space ◽  
Random Method ◽  
Better Than

Differential search algorithm (DS) is a relatively new evolutionary algorithm inspired by the Brownian-like random-walk movement which is used by an organism to migrate. It has been verified to be more effective than ABC, JDE, JADE, SADE, EPSDE, GSA, PSO2011, and CMA-ES. In this paper, we propose four improved solution search algorithms, namely “DS/rand/1,” “DS/rand/2,” “DS/current to rand/1,” and “DS/current to rand/2” to search the new space and enhance the convergence rate for the global optimization problem. In order to verify the performance of different solution search methods, 23 benchmark functions are employed. Experimental results indicate that the proposed algorithm performs better than, or at least comparable to, the original algorithm when considering the quality of the solution obtained. However, these schemes cannot still achieve the best solution for all functions. In order to further enhance the convergence rate and the diversity of the algorithm, a composite differential search algorithm (CDS) is proposed in this paper. This new algorithm combines three new proposed search schemes including “DS/rand/1,” “DS/rand/2,” and “DS/current to rand/1” with three control parameters using a random method to generate the offspring. Experiment results show that CDS has a faster convergence rate and better search ability based on the 23 benchmark functions.

Study on Free Search for Combinatorial Optimization Problem

2013 ◽  
Vol 411-414 ◽  
pp. 1904-1910
Author(s):  
Kai Zhong Jiang ◽  
Tian Bo Wang ◽  
Zhong Tuan Zheng ◽  
Yu Zhou
Keyword(s):  
Combinatorial Optimization ◽  
Optimization Problem ◽  
Feasible Solution ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Search Process ◽  
Standard Data ◽  
Combinatorial Optimization Problems ◽  
Free Search

An algorithm based on free search is proposed for the combinatorial optimization problems. In this algorithm, a feasible solution is converted into a full permutation of all the elements and a transformation of one solution into another solution can be interpreted the transformation of one permutation into another permutation. Then, the algorithm is combined with intersection elimination. The discrete free search algorithm greatly improves the convergence rate of the search process and enhances the quality of the results. The experiment results on TSP standard data show that the performance of the proposed algorithm is increased by about 2.7% than that of the genetic algorithm.

scholarly journals A Deviation-Based Dynamic Vertex Reordering Technique for 2D Mesh Quality Improvement

Symmetry ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 895 ◽  
Author(s):  
Junhyeok Choi ◽  
Harrim Kim ◽  
Shankar Prasad Sastry ◽  
Jibum Kim
Keyword(s):  
Quality Improvement ◽  
Optimization Problem ◽  
Mesh Optimization ◽  
Mesh Quality ◽  
Free Vertex ◽  
Smooth Optimization ◽  
Downhill Simplex ◽  
Mesh Quality Improvement ◽  
Better Than

We propose a novel deviation-based vertex reordering method for 2D mesh quality improvement. We reorder free vertices based on how likely this is to improve the quality of adjacent elements, based on the gradient of the element quality with respect to the vertex location. Specifically, we prioritize the free vertex with large differences between the best and the worst-quality element around the free vertex. Our method performs better than existing vertex reordering methods since it is based on the theory of non-smooth optimization. The downhill simplex method is employed to solve the mesh optimization problem for improving the worst element quality. Numerical results show that the proposed vertex reordering techniques improve both the worst and average element, compared to those with existing vertex reordering techniques.

scholarly journals Simulated Annealing with Exploratory Sensing for Global Optimization

Algorithms ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 230
Author(s):  
Majid Almarashi ◽  
Wael Deabes ◽  
Hesham H. Amin ◽  
Abdel-Rahman Hedar
Keyword(s):  
Global Optimization ◽  
Simulated Annealing ◽  
Initial Temperature ◽  
Search Algorithm ◽  
Random Search ◽  
Search Space ◽  
Search Algorithms ◽  
Two Phases ◽  
Number Of Iterations

Simulated annealing is a well-known search algorithm used with success history in many search problems. However, the random walk of the simulated annealing does not benefit from the memory of visited states, causing excessive random search with no diversification history. Unlike memory-based search algorithms such as the tabu search, the search in simulated annealing is dependent on the choice of the initial temperature to explore the search space, which has little indications of how much exploration has been carried out. The lack of exploration eye can affect the quality of the found solutions while the nature of the search in simulated annealing is mainly local. In this work, a methodology of two phases using an automatic diversification and intensification based on memory and sensing tools is proposed. The proposed method is called Simulated Annealing with Exploratory Sensing. The computational experiments show the efficiency of the proposed method in ensuring a good exploration while finding good solutions within a similar number of iterations.

scholarly journals Application of Improved Sparrow Search Algorithm in Concrete

2021 ◽  
Vol 2082 (1) ◽  
pp. 012014
Author(s):  
Chengtian Ouyang ◽  
Feng Tang ◽  
Donglin Zhu ◽  
Yaxian Qiu ◽  
Yujia Liu
Keyword(s):  
Local Search ◽  
High Stability ◽  
Search Algorithm ◽  
Concrete Strength ◽  
Optimal Solution ◽  
Search Algorithms ◽  
Strength Prediction

Abstract Compared with other algorithms, the performance of sparrow algorithm is better, but it also has shortcomings such as insufficient convergence and large randomness. For this reason, this paper proposes an improved sparrow search algorithm, which uses K-means to initialize the population to reduce the influence of randomness. Use sine and cosine search to improve the quality of the position of followers, and finally use adaptive local search to update the optimal solution, and apply it to concrete strength prediction. The results show that various improved sparrow search algorithms have certain advantages and high stability.

scholarly journals A Meta-Heuristic Search Algorithm based on Infrasonic Mating Displays in Peafowls

2021 ◽  
pp. 98-106
Author(s):  
Kenekayoro Patrick
Keyword(s):  
Search Algorithm ◽  
Mating Behaviour ◽  
Gravitational Search Algorithm ◽  
Solution Space ◽  
Search Algorithms ◽  
Heuristic Search Algorithm ◽  
Intractable Problems ◽  
Gravitational Search ◽  
Optimization Search ◽  
Better Than

Meta-heuristic techniques are important as they are used to find solutions to computationally intractable problems. Simplistic methods such as exhaustive search become computationally expensive and unreliable as the solution space for search algorithms increase. As no method is guaranteed to perform better than all others in all classes of optimization search problems, there is a need to constantly find new and/or adapt old search algorithms. This research proposes an Infrasonic Search Algorithm, inspired from the Gravitational Search Algorithm and the mating behaviour in peafowls. The Infrasonic Search Algorithm identified competitive solutions to 23 benchmark unimodal and multimodal test functions compared to the Genetic Algorithm, Particle Swarm Optimization Algorithm and the Gravitational Search Algorithm.

A Hybrid Hypergraph Partitioning Algorithm for Scientific Computing

2013 ◽  
Vol 753-755 ◽  
pp. 2900-2903
Author(s):  
Yao Yuan Zeng ◽  
Wen Tao Zhao ◽  
Zheng Hua Wang
Keyword(s):  
Scientific Computing ◽  
Simulated Annealing Algorithm ◽  
Search Algorithm ◽  
Research Topic ◽  
Hypergraph Partitioning ◽  
Benchmark Suite ◽  
Annealing Algorithm ◽  
Partitioning Algorithm ◽  
Better Than

Hypergraph partitioning is an increasingly important and widely studied research topic in parallel scientific computing. In this paper, we present a multiway hypergraph partitioning algorithm, mixed simulated annealing algorithm for global optimization and tabu search algorithm for local optimization. Experiments on the benchmark suite of several unstructured meshes show that, for 2-, 4-, 8-, 16-and 32-way partitioning, the quality of partition produced by our algorithm are on the average 6% and the maximum 17% better than those produced by partitioning software hMETIS in term of the cutsize metric.

scholarly journals Autofocusing Optimal Search Algorithm for a Telescope System

2021 ◽  
pp. 2150012
Author(s):  
Islam Helmy ◽  
Alaa Hamdy ◽  
Doaa Eid ◽  
Ahmed Shokry
Keyword(s):  
Search Algorithm ◽  
Search Algorithms ◽  
Binary Search ◽  
Imaging Systems ◽  
Search Process ◽  
Optimal Search ◽  
Astronomical Observations ◽  
Telescope System ◽  
Auto Focusing

Focus accuracy affects the quality of the astronomical observations. Auto-focusing is necessary for imaging systems designed for astronomical observations. The automatic focus system searches for the best focus position by using a proposed search algorithm. The search algorithm uses the image’s focus levels as its objective function in the search process. This paper aims to study the performance of several search algorithms to select a suitable one. The proper search algorithm will be used to develop an automatic focus system for Kottamia Astronomical Observatory (KAO). The optimal search algorithm is selected by applying several search algorithms into five sequences of star-clusters observations. Then, their performance is evaluated based on two criteria, which are accuracy and number of steps. The experimental results show that the Binary search is the optimal search algorithm.

Trajectory optimization for manipulators based on external archives self-searching multi-objective particle swarm optimization

2021 ◽  
pp. 095440622199748
Author(s):  
Youyu Liu ◽  
Xuyou Zhang
Keyword(s):  
Particle Swarm Optimization ◽  
Trajectory Optimization ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
B Splines ◽  
Multi Objective ◽  
Searching Strategy ◽  
Better Than

In order to improve the quality of the non-inferior solutions obtained by multi-objective particle swarm optimization (MOPSO), an improved algorithm called external archives self-searching multi-objective particle swarm optimization (EASS-MOPSO) was proposed and applied to a multi-objective trajectory optimization problem for manipulators. The position curves of joints were constructed by using quartic B-splines; the mathematical models of time, energy and jerk optimization objectives for manipulators were established; and the kinematic constraints of joints were transformed into the constraints of the control vertexes of the B-splines. A self-searching strategy of external archives to make non-inferior solutions have the ability to search the surrounding hyperspace was explored, and a diversity maintaining strategy of the external archives was proposed. The results of several test functions by simulation show that the convergence and diversity of the proposed algorithm are better than those of other 4 selected algorithms; the results of the trajectory optimization problem for manipulators by simulation show that the convergence, diversity and time consumption of the proposed algorithm are significantly better than those of non-dominated sorting genetic algorithm.

scholarly journals OPTIMIZATION OF GRILLAGES USING GENETIC ALGORITHMS FOR INTEGRATING MATLAB AND FORTRAN ENVIRONMENTS / MATLAB IR FORTRAN APLINKŲ SUJUNGIMAS ROSTVERKAMS OPTIMIZUOTI GENETINIAIS ALGORITMAIS

2013 ◽  
Vol 6 (4) ◽  
pp. 564-568
Author(s):  
Darius Mačiūnas ◽  
Juozas Kauna ◽  
Dmitrij Šešok
Keyword(s):  
Genetic Algorithm ◽  
Global Optimization ◽  
Optimization Problem ◽  
Optimization Procedure ◽  
Optimal Layout ◽  
Reactive Force ◽  
Topology Optimization Problem ◽  
Original Algorithm ◽  
Fortran Programming Language

The purpose of the paper is to present technology applied for the global optimization of grillage-type pile foundations (further grillages). The goal of optimization is to obtain the optimal layout of pile placement in the grillages. The problem can be categorized as a topology optimization problem. The objective function is comprised of maximum reactive force emerging in a pile. The reactive force is minimized during the procedure of optimization during which variables enclose the positions of piles beneath connecting beams. Reactive forces in all piles are computed utilizing an original algorithm implemented in the Fortran programming language. The algorithm is integrated into the MatLab environment where the optimization procedure is executed utilizing a genetic algorithm. The article also describes technology enabling the integration of MatLab and Fortran environments. The authors seek to evaluate the quality of a solution to the problem analyzing experimental results obtained applying the proposed technology. Santrauka Straipsnyje pateikiama sijynų tipo pamatų (toliau sijynų) globalaus optimizavimo technologija. Optimizavimo tikslas – nustatyti optimalų polių išdėstymą sijynuose. Šis uždavinys priskiriamas topologijos optimizavimo uždavinių grupei. Tikslo funkciją sudaro maksimali poliuje kylanti atraminė reakcijos jėga, kuri minimizuojama optimizavimo procese. Šio uždavinio projektavimo kintamieji - polių padėtys po jungiančiosiomis sijyno sijomis. Tiesioginis reakcijų poliuose skaičiavimo uždavinys sprendžiamas originaliu algoritmu, sukurtu Fortran programavimo kalba. Šis algoritmas juodosios dėžės principu jungiamas prie MatLab aplinkos, kurioje genetiniu algoritmu sprendžiamas optimizavimo uždavinys. Straipsnyje taip pat aprašyta technologija, kuri leidžia sujungti Matlab ir Fortran aplinkas, t. y. iš Matlab aplinkos iškviesti Fortran paprogramį. Analizuodami eksperimentinius duomenis autoriai bando įvertinti gaunamų sprendinių kokybę.

FluidC+: A novel community detection algorithm based on fluid propagation

2019 ◽  
Vol 30 (04) ◽  
pp. 1950021
Author(s):  
Jinfang Sheng ◽  
Kai Wang ◽  
Zejun Sun ◽  
Jie Hu ◽  
Bin Wang ◽  
...  
Keyword(s):  
Community Detection ◽  
Real World ◽  
Seed Set ◽  
State Of The Art ◽  
Ground Truth ◽  
Detection Algorithm ◽  
Original Algorithm ◽  
Community Detection Algorithm ◽  
Better Than

In recent years, community detection has gradually become a hot topic in the complex network data mining field. The research of community detection is helpful not only to understand network topology structure but also to explore network hiding function. In this paper, we improve FluidC which is a novel community detection algorithm based on fluid propagation, by ameliorating the quality of seed set based on positive feedback and determining the node update order. We first summarize the shortcomings of FluidC and analyze the reasons result in these drawbacks. Then, we took some effective measures to overcome them and proposed an efficient community detection algorithm, called FluidC+. Finally, experiments on the generated network and real-world network show that our method not only greatly improves the performance of the original algorithm FluidC but also is better than many state-of-the-art algorithms, especially in the performance on real-world network with ground truth.

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