Diagnostic Strategy Optimization Based on Predatory Search Algorithm

2012 ◽  
Vol 591-593 ◽  
pp. 2441-2444
Author(s):  
Jin Luo ◽  
Qi Bin Deng ◽  
Chen Meng
Keyword(s):  
Local Search ◽  
Objective Function ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Space ◽  
Global Search ◽  
State Probability ◽  
Diagnostic Strategy ◽  
Local Optimal Solution ◽  
Predatory Search Algorithm

With respect to the inherent NP-hard complexity of Optimization of testability diagnostic strategy problem, a predatory search algorithm simulating animal predatory strategies was designed. This algorithm adopted the gross test expense including state probability, isolation matrix and test expense as its objective function, defined local and global search by the restriction value of search space based on two points exchange, and realized the conversion between local and global search by adjusting the restriction value of search space. It had better ability to conduct local search and jump out of local optimal solution simultaneously, and provided a better resolution for the optimization of testability diagnostic strategy.

Design of 3D-Route Planning Based on the Improvement of Simple Genetic Algorithm

2013 ◽  
Vol 760-762 ◽  
pp. 1690-1694
Author(s):  
Jian Xia Zhang ◽  
Tao Yu ◽  
Ji Ping Chen ◽  
Ying Hao Lin ◽  
Yu Meng Zhang
Keyword(s):  
Genetic Algorithm ◽  
Search Algorithm ◽  
Scientific Research ◽  
Route Planning ◽  
Optimal Solution ◽  
Global Search ◽  
Global Search Algorithm ◽  
Simple Genetic Algorithm ◽  
Local Optimal Solution ◽  
Simulation Results

With the wide application of UAV in the scientific research,its route planning is becoming more and more important. In order to design the best route planning when UAV operates in the field, this paper mainly puts to use the simple genetic algorithm to design 3D-route planning. It primarily introduces the advantages of genetic algorithm compared to others on the designing of route planning. The improvement of simple genetic algorithm is because of the safety of UAV when it flights higher, and the 3D-route planning should include all the corresponding areas. The simulation results show that: the improvement of simple genetic algorithm gets rid of the dependence of parameters, at the same time it is a global search algorithm to avoid falling into the local optimal solution. Whats more, it can meet the requirements of the 3D-route planning design, to the purpose of regional scope and high safety.

The Research on Modified K-Means Algorithm Based on GA&SA

2013 ◽  
Vol 347-350 ◽  
pp. 3242-3246
Author(s):  
Zhe Feng Zhu ◽  
Xiao Bin Hui ◽  
Yi Qian Cao ◽  
Wan Xiang Lian
Keyword(s):  
Local Search ◽  
Clustering Algorithm ◽  
Optimal Solution ◽  
Global Search ◽  
Local Optimization ◽  
Effective Algorithm ◽  
Global Optimal Solution ◽  
Local Optimal Solution ◽  
Global Optimal ◽  
Clustering Center

The traditional K-means clustering algorithm has the disadvantage of weakness in overall search, easily falling into local optimization, highly reliance on initial clustering center. Aiming at the drawback of falling into partial optimization, putting forward a modified K-means algorithm mixing GA and SA, which combined the advantages of global search ability of GA and local search, to avoid K-means algorithm to lost into local optimal solution. The results of simulation show that the performance of above-mentioned algorithm is better in the optimization capacity than before, and easier to get the global optimal solution. It is an effective algorithm.

Solution Attractor of Local Search System: A Method to Reduce Computational Complexity of the Traveling Salesman Problem

2020 ◽  
Author(s):  
Weiqi Li
Keyword(s):  
Computational Complexity ◽  
Local Search ◽  
Traveling Salesman Problem ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Solution Space ◽  
Search Space ◽  
Traveling Salesman ◽  
Search System ◽  
The Traveling Salesman Problem

The traveling salesman problem (TSP) is presumably difficult to solve exactly using local search algorithms. It can be exactly solved by only one algorithm—the enumerative search algorithm. However, the scanning of all possible solutions requires exponential computing time. Do we need exploring all the possibilities to find the optimal solution? How can we narrow down the search space effectively and efficiently for an exhausted search? This chapter attempts to answer these questions. A local search algorithm is a discrete dynamical system, in which a search trajectory searches a part of the solution space and stops at a locally optimal point. A solution attractor of a local search system for the TSP is defined as a subset of the solution space that contains all locally optimal tours. The solution attractor concept gives us great insight into the computational complexity of the TSP. If we know where the solution attractor is located in the solution space, we simply completely search the solution attractor, rather than the entire solution space, to find the globally optimal tour. This chapter describes the solution attractor of local search system for the TSP and then presents a novel search system—the attractor-based search system—that can solve the TSP much efficiently with global optimality guarantee.

scholarly journals Improved Sparrow Search Algorithm Based on Iterative Local Search

2021 ◽  
Vol 2021 ◽  
pp. 1-31
Author(s):  
Shaoqiang Yan ◽  
Ping Yang ◽  
Donglin Zhu ◽  
Wanli Zheng ◽  
Fengxuan Wu
Keyword(s):  
Local Search ◽  
Learning Strategy ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Parameter Tuning ◽  
Search Performance ◽  
Search Range ◽  
Variable Helix ◽  
Local Optimal Solution ◽  
Local Search Strategy

This paper solves the shortcomings of sparrow search algorithm in poor utilization to the current individual and lack of effective search, improves its search performance, achieves good results on 23 basic benchmark functions and CEC 2017, and effectively improves the problem that the algorithm falls into local optimal solution and has low search accuracy. This paper proposes an improved sparrow search algorithm based on iterative local search (ISSA). In the global search phase of the followers, the variable helix factor is introduced, which makes full use of the individual’s opposite solution about the origin, reduces the number of individuals beyond the boundary, and ensures the algorithm has a detailed and flexible search ability. In the local search phase of the followers, an improved iterative local search strategy is adopted to increase the search accuracy and prevent the omission of the optimal solution. By adding the dimension by dimension lens learning strategy to scouters, the search range is more flexible and helps jump out of the local optimal solution by changing the focusing ability of the lens and the dynamic boundary of each dimension. Finally, the boundary control is improved to effectively utilize the individuals beyond the boundary while retaining the randomness of the individuals. The ISSA is compared with PSO, SCA, GWO, WOA, MWOA, SSA, BSSA, CSSA, and LSSA on 23 basic functions to verify the optimization performance of the algorithm. In addition, in order to further verify the optimization performance of the algorithm when the optimal solution is not 0, the above algorithms are compared in CEC 2017 test function. The simulation results show that the ISSA has good universality. Finally, this paper applies ISSA to PID parameter tuning and robot path planning, and the results show that the algorithm has good practicability and effect.

scholarly journals An evolutionary-based optimization algorithm for truss sizing design

2016 ◽  
Vol 38 (4) ◽  
pp. 307-317
Author(s):  
Pham Hoang Anh
Keyword(s):  
Local Search ◽  
Objective Function ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Optimal Solution ◽  
The Other ◽  
Truss Structures ◽  
Benchmark Problems ◽  
Discrete Variables ◽  
Objective Functions

In this paper, the optimal sizing of truss structures is solved using a novel evolutionary-based optimization algorithm. The efficiency of the proposed method lies in the combination of global search and local search, in which the global move is applied for a set of random solutions whereas the local move is performed on the other solutions in the search population. Three truss sizing benchmark problems with discrete variables are used to examine the performance of the proposed algorithm. Objective functions of the optimization problems are minimum weights of the whole truss structures and constraints are stress in members and displacement at nodes. Here, the constraints and objective function are treated separately so that both function and constraint evaluations can be saved. The results show that the new algorithm can find optimal solution effectively and it is competitive with some recent metaheuristic algorithms in terms of number of structural analyses required.

Modified Sub-Population Based Heat Transfer Search Algorithm for Structural Optimization

2017 ◽  
Vol 8 (3) ◽  
pp. 1-23 ◽  
Author(s):  
Ghanshyam Tejani ◽  
Vimal Savsani ◽  
Vivek Patel
Keyword(s):  
Heat Transfer ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Population Based ◽  
Truss Optimization ◽  
Feasible Region ◽  
Frequency Constraints ◽  
Local Optimal Solution ◽  
Better Than

In this study, a modified heat transfer search (MHTS) algorithm is proposed by incorporating sub-population based simultaneous heat transfer modes viz. conduction, convection, and radiation in the basic HTS algorithm. However, the basic HTS algorithm considers only one of the modes of heat transfer for each generation. The multiple natural frequency constraints in truss optimization problems can improve the dynamic behavior of the structure and prevent undesirable vibrations. However, shape and size variables subjected to frequency constraints are difficult to handle due to the complexity of its feasible region, which is non-linear, non-convex, implicit, and often converging to the local optimal solution. The viability and effectiveness of the HTS and MHTS algorithms are investigated by six standard trusses problems. The solutions illustrate that the MHTS algorithm performs better than the HTS algorithm.

GLOBAL OPTIMIZATION OF GRILLAGES WITH A HYBRID GENETIC ALGORITHM

2009 ◽  
Vol 1 (2) ◽  
pp. 80-88 ◽  
Author(s):  
Dmitrij Šešok ◽  
Rimantas Belevičius
Keyword(s):  
Genetic Algorithm ◽  
Finite Element ◽  
Local Search ◽  
Objective Function ◽  
Search Algorithm ◽  
Hybrid Genetic Algorithm ◽  
Design Parameters ◽  
Finite Element Program ◽  
One Dimensional ◽  
Element Program

Aim of the article is to suggest technology for optimization of pile positions in a grillage-type foundations seeking for the minimum possible pile quantity. The objective function to be minimized is the largest reactive force that arises in any pile under the action of statical loading. When piles of the grillage have different characteristics, the alternative form of objective function may be employed: the largest difference between vertical reaction and allowable reaction at any pile. Several different allowable reactions with a given number of such piles may be intended for a grillage. The design parameters for the problem are positions of the piles. The feasible space of design parameters is determined by two constraints. First, during the optimization process piles can move only along the connecting beams. Therefore, the two-dimensional grillage is “unfolded” to a one-dimensional construct, and the supports are allowed to range through this space freely. Second, the minimum allowable distance between two adjacent piles is introduced due to the specific capacities of pile driver.The initial data for the problem are the following: the geometrical scheme of the grillage, the cross-section and material data of connecting beams, minimum possible distance between adjacent supports, characteristics of piles, and the loading data given in the form of concentrated loads or trapezoidal distributed loadings. The results of solution are the required number of piles and their positions.The entire optimization problem is solved in two steps. First, the grillage is transformed to a one-dimensional construct, and the optimizer decides about a routine solution (i.e. the positions of piles in this construct). Second, the backward transformation returns the pile positions into the two-dimensional grillage, and the “black-box” finite element program returns the corresponding objective function value. On the basis of this value the optimizer predicts the new positions of piles, etc. The finite element program idealizes the connecting beams as the beam elements and the piles – as the finite element mesh nodes with a given boundary conditions in form of vertical and rotational stiffnesses. The optimizing program is an elitist genetic algorithm or a random local search algorithm. At the beginning of problem solution the genetic algorithm is employed. In the optimization problems under consideration, the genetic algorithms usually demonstrate very fast convergence at the beginning of solution and slow non-monotonic convergence to a certain local solution point after some number of generations. When the further solution with a genetic algorithm refuses to improve the achieved answer, i.e. a certain local solution is obtained; the specific random search algorithm is used. The moment, at which the transition from genetic algorithm to the local search is optimal, is sought in the paper analyzing the experimental data. Thus, the hybrid genetic algorithm that combines the genetic algorithm itself and the local search is suggested for the optimization of grillages.

scholarly journals SOLVING INTEGER PROGRAMMING PROBLEMS BY USING POPULATION-BASED BEETLE ANTENNAE SEARCH ALGORITHM

2021 ◽  
Vol 9 (3-4) ◽  
pp. 89-99
Author(s):  
Ivona Brajević ◽  
Miodrag Brzaković ◽  
Goran Jocić
Keyword(s):  
Integer Programming ◽  
Optimization Problems ◽  
Mechanical Design ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Search Space ◽  
Population Based ◽  
Local Optimum ◽  
Integer Programming Problems ◽  
Metaheuristic Techniques

Beetle antennae search (BAS) algorithm is a newly proposed single-solution based metaheuristic technique inspired by the beetle preying process. Although BAS algorithm has shown good search abilities, it can be easily trapped into local optimum when it is used to solve hard optimization problems. With the intention to overcome this drawback, this paper presents a population-based beetle antennae search (PBAS) algorithm for solving integer programming problems.  This method employs the population's capability to search diverse regions of the search space to provide better guarantee for finding the optimal solution. The PBAS method was tested on nine integer programming problems and one mechanical design problem. The proposed algorithm was compared to other state-of-the-art metaheuristic techniques. The comparisons show that the proposed PBAS algorithm produces better results for majority of tested problems.  

scholarly journals Real power loss diminution by predestination of particles wavering search algorithm

2020 ◽  
Vol 9 (2) ◽  
pp. 92
Author(s):  
Kanagasabai Lenin
Keyword(s):  
Power Loss ◽  
Reactive Power ◽  
Search Algorithm ◽  
Optimal Solution ◽  
Real Power ◽  
Gradient Based ◽  
Local Optimal Solution ◽  
Simulation Results ◽  
Power Problem ◽  
Steady Velocity

In this work Predestination of Particles Wavering Search (PPS) algorithm has been applied to solve optimal reactive power problem. PPS algorithm has been modeled based on the motion of the particles in the exploration space. Normally the movement of the particle is based on gradient and swarming motion.  Particles are permitted to progress in steady velocity in gradient-based progress, but when the outcome is poor when compared to previous upshot, immediately particle rapidity will be upturned with semi of the magnitude and it will help to reach local optimal solution and it is expressed as wavering movement. In standard IEEE 14, 30, 57,118,300 bus systems Proposed Predestination of Particles Wavering Search (PPS) algorithm is evaluated and simulation results show the PPS reduced the power loss efficiently.

scholarly journals VIBRANT SEARCH MECHANISM FOR NUMERICAL OPTIMIZATION FUNCTIONS

2018 ◽  
Author(s):  
Moh’d Khaled Yousef Shambour
Keyword(s):  
Convergence Rates ◽  
Search Algorithm ◽  
Harmony Search ◽  
Optimal Solution ◽  
Search Space ◽  
Harmony Search Algorithm ◽  
Search Performance ◽  
Search Spaces ◽  
Search Mechanism ◽  
Heuristic Technique

Recently, various variants of evolutionary algorithms have been offered to optimize the exploration and exploitation abilities of the search mechanism. Some of these variants still suffer from slow convergence rates around the optimal solution. In this paper, a novel heuristic technique is introduced to enhance the search capabilities of an algorithm, focusing on certain search spaces during evolution process. Then, employing a heuristic search mechanism that scans an entire space before determining the desired segment of that search space. The proposed method randomly updates the desired segment by monitoring the algorithm search performance levels on different search space divisions. The effectiveness of the proposed technique is assessed through harmony search algorithm (HSA). The performance of this mechanism is examined with several types of benchmark optimization functions, and the results are compared with those of the classic version and two variants of HSA. The experimental results demonstrate that the proposed technique achieves the lowest values (best results) in 80% of the non-shifted functions, whereas only 33.3% of total experimental cases are achieved within the shifted functions in a total of 30 problem dimensions. In 100 problem dimensions, 100% and 25% of the best results are reported for non-shifted and shifted functions, respectively. The results reveal that the proposed technique is able to orient the search mechanism toward desired segments of search space, which therefore significantly improves the overall search performance of HSA, especially for non-shifted optimization functions.   

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