Balancing the Degree of Exploration and Exploitation of Swarm Intelligence Using Parallel Computing

2019 ◽  
Vol 28 (03) ◽  
pp. 1950014
Author(s):  
Surafel Luleseged Tilahun
Keyword(s):  
Parallel Computing ◽  
Objective Function ◽  
Swarm Intelligence ◽  
Parallel Implementation ◽  
Metaheuristic Algorithms ◽  
Feasible Region ◽  
Central Research ◽  
Exploration And Exploitation ◽  
Specific Distribution ◽  
Search Behaviors

The two basic search behaviors used in metaheuristic algorithms in general and swarm intelligence in particular are exploration and exploitation. Exploration refers to searching the unexplored area of the feasible region while exploitation refers to the search of the neighborhood of a promising region. The success of these algorithms highly depends on how these two search behaviors are balanced. Increasing the number of initial solutions indeed increase the performance of the algorithm over the expense of the runtime. Different exploration and exploitation adjustments are used in different metaheuristic algorithms. To well balance the degree of exploration and exploitation, which ultimately leads to finding good solutions, needs a careful implementation and sufficient runtime. However, the runtime is another issue which has been a bottleneck for different application, especially for high dimensional, highly constrained and expensive objective function cases. Using parallel computing to overcome this limitation has been a central research issue in the study of metaheuristic algorithms. The three basic parallelization approaches used are the parallel move model which is a master slave model where solutions will be distributed to different processors for updating, parallel multi start model which uses several updating methods for the solutions in parallel and move acceleration method, which refers to a parallel implementation with parallelizing the objective function. There is no specific parallelization used towards balancing of exploration and exploitation is mentioned. Hence, in this paper a parallel implementation of swarm intelligence in general and two algorithms, namely particle swarm optimization and prey predator algorithm, in particular will be discussed. The parallelization proposed is aimed to merge the parallel move model and the parallel multi start model with the aim of adjusting the degree of exploration and exploitation. Experimental study based on ten benchmark problems from constrained as well as unconstrained problems will be used for simulation. It will be shown that the parallelization with specific distribution of exploration and exploitation runs better and efficiently compared to the serial and also the parallel version without exporation/exploitation adjustment.

scholarly journals On validation of solutions to linear programming problems on cluster computing systems

2021 ◽  
pp. 252-261
Author(s):  
Л.Б. Соколинский ◽  
И.М. Соколинская
Keyword(s):  
Linear Programming ◽  
Objective Function ◽  
Large Scale ◽  
Cluster Computing ◽  
Parallel Implementation ◽  
Main Idea ◽  
Small Radius ◽  
Feasible Region ◽  
Computing Systems ◽  
Scalable Algorithm

В статье представлен параллельный алгоритм валидации решений задач линейного программирования. Идея метода состоит в том, чтобы генерировать регулярный набор точек на гиперсфере малого радиуса, центрированной в точке тестируемого решения. Целевая функция вычисляется для каждой точки валидационного множества, принадлежащей допустимой области. Если все полученные значения меньше или равны значению целевой функции в точке, проверяемой как решение, то эта точка считается корректным решением. Параллельная реализация алгоритма VaLiPro выполнена на языке C++ с использованием параллельного BSF-каркаса, инкапсулирующего в проблемно-независимой части своего кода все аспекты, связанные с распараллеливанием программы на базе библиотеки MPI. Приводятся результаты масштабных вычислительных экспериментов на кластерной вычислительной системе, подтверждающие эффективность предложенного подхода. The paper presents and evaluates a scalable algorithm for validating solutions to linear programming (LP) problems on cluster computing systems. The main idea of the method is to generate a regular set of points (validation set) on a small-radius hypersphere centered at the solution point submitted to validation. The objective function is computed at each point of the validation that belongs to the feasible region. If all the values are less than or equal to the value of the objective function at the point that is to be validated, then this point is the correct solution. The parallel implementation of the VaLiPro algorithm is written in C++ through the parallel BSF-skeleton, which encapsulates all aspects related to the MPI-based parallelization of the program. We provide the results of large-scale computational experiments on a cluster computing system to study the scalability of the VaLiPro algorithm.

scholarly journals Research on the inverse kinematics of manipulator using an improved self-adaptive mutation differential evolution algorithm

2021 ◽  
Vol 18 (3) ◽  
pp. 172988142110144
Author(s):  
Qianqian Zhang ◽  
Daqing Wang ◽  
Lifu Gao
Keyword(s):  
Differential Evolution ◽  
Objective Function ◽  
Inverse Kinematics ◽  
Differential Evolution Algorithm ◽  
Weight Coefficient ◽  
Adaptive Mutation ◽  
Feasible Region ◽  
Serial Manipulators ◽  
De Algorithm ◽  
Self Adaptive

To assess the inverse kinematics (IK) of multiple degree-of-freedom (DOF) serial manipulators, this article proposes a method for solving the IK of manipulators using an improved self-adaptive mutation differential evolution (DE) algorithm. First, based on the self-adaptive DE algorithm, a new adaptive mutation operator and adaptive scaling factor are proposed to change the control parameters and differential strategy of the DE algorithm. Then, an error-related weight coefficient of the objective function is proposed to balance the weight of the position error and orientation error in the objective function. Finally, the proposed method is verified by the benchmark function, the 6-DOF and 7-DOF serial manipulator model. Experimental results show that the improvement of the algorithm and improved objective function can significantly improve the accuracy of the IK. For the specified points and random points in the feasible region, the proportion of accuracy meeting the specified requirements is increased by 22.5% and 28.7%, respectively.

scholarly journals An alternative perspective on copositive and convex relaxations of nonconvex quadratic programs

2021 ◽  
Author(s):  
E. Alper Yıldırım
Keyword(s):  
Objective Function ◽  
Convex Relaxation ◽  
Large Family ◽  
Quadratic Program ◽  
Feasible Region ◽  
Recession Cone ◽  
Convex Relaxations ◽  
Quadratic Programs ◽  
Alternative Perspective ◽  
Optimal Value

AbstractWe study convex relaxations of nonconvex quadratic programs. We identify a family of so-called feasibility preserving convex relaxations, which includes the well-known copositive and doubly nonnegative relaxations, with the property that the convex relaxation is feasible if and only if the nonconvex quadratic program is feasible. We observe that each convex relaxation in this family implicitly induces a convex underestimator of the objective function on the feasible region of the quadratic program. This alternative perspective on convex relaxations enables us to establish several useful properties of the corresponding convex underestimators. In particular, if the recession cone of the feasible region of the quadratic program does not contain any directions of negative curvature, we show that the convex underestimator arising from the copositive relaxation is precisely the convex envelope of the objective function of the quadratic program, strengthening Burer’s well-known result on the exactness of the copositive relaxation in the case of nonconvex quadratic programs. We also present an algorithmic recipe for constructing instances of quadratic programs with a finite optimal value but an unbounded relaxation for a rather large family of convex relaxations including the doubly nonnegative relaxation.

Population Diversity of Particle Swarm Optimization Algorithm on Solving Single and Multi-Objective Problems

2020 ◽  
pp. 312-344
Author(s):  
Shi Cheng ◽  
Yuhui Shi ◽  
Quande Qin
Keyword(s):  
Particle Swarm Optimization ◽  
Swarm Intelligence ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Population Diversity ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Diversity Measures ◽  
Computational Resources ◽  
Swarm Intelligence Algorithm

Premature convergence occurs in swarm intelligence algorithms searching for optima. A swarm intelligence algorithm has two kinds of abilities: the exploration of new possibilities and the exploitation of old certainties. The exploration ability means that an algorithm can explore more search places to increase the possibility that the algorithm can find good enough solutions. In contrast, the exploitation ability means that an algorithm focuses on the refinement of found promising areas. An algorithm should have a balance between exploration and exploitation, that is, the allocation of computational resources should be optimized to ensure that an algorithm can find good enough solutions effectively. The diversity measures the distribution of individuals' information. From the observation of the distribution and diversity change, the degree of exploration and exploitation can be obtained.

Parameter Selection for Swarm Intelligence Algorithms

2018 ◽  
Vol 9 (4) ◽  
pp. 1-20
Author(s):  
Breno A. M. Menezes ◽  
Fabian Wrede ◽  
Herbert Kuchen ◽  
Fernando B. Lima Neto
Keyword(s):  
Population Size ◽  
Swarm Intelligence ◽  
Execution Time ◽  
Optimization Problems ◽  
Parallel Implementation ◽  
Fish School ◽  
Size Number ◽  
Complex Optimization ◽  
Selection For ◽  
Number Of Iterations

Swarm intelligence (SI) algorithms are handy tools for solving complex optimization problems. When problems grow in size and complexity, an increase in population or number of iterations might be required in order to achieve a good solution. These adjustments also impact the execution time. This article investigates the trade-off involving population size, number of iterations and problem complexity, aiming to improve the efficiency of SI algorithms. Results based on a parallel implementation of Fish School Search show that increasing the population size is beneficial for finding good solutions. However, we observed an asymptotic behavior, i.e. increasing the population over a certain threshold only leads to slight improvements. Furthermore, the execution time was analyzed.

A new solution method for a class of large dimension rank-two nonconvex programs

2020 ◽  
Author(s):  
Riccardo Cambini ◽  
Irene Venturi
Keyword(s):  
Objective Function ◽  
Optimal Level ◽  
Outer Approximation ◽  
Point Of View ◽  
Large Dimension ◽  
Low Rank ◽  
Feasible Region ◽  
Quantitative Management ◽  
Solution Methods ◽  
Optimal Level Solutions

Abstract Low-rank problems are nonlinear minimization problems in which the objective function, by means of a suitable linear transformation of the variables, depends on very few variables. These problems often arise in quantitative management science applications, for example, in location models, transportation problems, production planning, data envelopment analysis and multiobjective programs. They are usually approached by means of outer approximation, branch and bound, branch and select and optimal level solution methods. The paper studies, from both a theoretical and an algorithmic point of view, a class of large-dimension rank-two nonconvex problems having a polyhedral feasible region and $f(x)=\phi (c^Tx+c_0,d^Tx+d_0)$ as the objective function. The proposed solution algorithm unifies a new partitioning method, an outer approximation approach and a mixed method. The results of a computational test are provided to compare these three approaches with the optimal level solutions method. In particular, the new partitioning method performs very well in solving large problems.

Comparative Study Between Two Swarm Intelligence Automatic Text Summaries

2018 ◽  
Vol 9 (1) ◽  
pp. 15-39 ◽  
Author(s):  
Mohamed Amine Boudia ◽  
Reda Mohamed Hamou ◽  
Abdelmalek Amine
Keyword(s):  
Comparative Study ◽  
Objective Function ◽  
Swarm Intelligence ◽  
The Other ◽  
Original Text ◽  
Simple Majority ◽  
Social Bees ◽  
The Third ◽  
Automatic Text ◽  
Previous Step

This article is a comparative study between two bio-inspired approach based on the swarm intelligence for automatic text summaries: Social Spiders and Social Bees. The authors use two techniques of extraction, one after the other: scoring of phrases, and similarity that aims to eliminate redundant phrases without losing the theme of the text. While the optimization use the bio-inspired approach to performs the results of the previous step. Its objective function of the optimization is to maximize the sum of similarity between phrases of the candidate summary in order to keep the theme of the text, minimize the sum of scores in order to increase the summarization rate; this optimization also will give a candidate's summary where the order of the phrases changes compared to the original text. The third and final step concerned in choosing a best summary from all candidates summaries generated by optimization layer, the authors opted for the technique of voting with a simple majority.

scholarly journals Application of Heuristic and Metaheuristic Algorithms in Solving Constrained Weber Problem with Feasible Region Bounded by Arcs

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Igor Stojanović ◽  
Ivona Brajević ◽  
Predrag S. Stanimirović ◽  
Lev A. Kazakovtsev ◽  
Zoran Zdravev
Keyword(s):  
Heuristic Algorithm ◽  
Optimization Problems ◽  
Continuous Optimization ◽  
Facility Location Problem ◽  
Metaheuristic Algorithms ◽  
Feasible Region ◽  
Weber Problem ◽  
Bee Colony ◽  
Feasible Solutions ◽  
Weiszfeld Procedure

The continuous planar facility location problem with the connected region of feasible solutions bounded by arcs is a particular case of the constrained Weber problem. This problem is a continuous optimization problem which has a nonconvex feasible set of constraints. This paper suggests appropriate modifications of four metaheuristic algorithms which are defined with the aim of solving this type of nonconvex optimization problems. Also, a comparison of these algorithms to each other as well as to the heuristic algorithm is presented. The artificial bee colony algorithm, firefly algorithm, and their recently proposed improved versions for constrained optimization are appropriately modified and applied to the case study. The heuristic algorithm based on modified Weiszfeld procedure is also implemented for the purpose of comparison with the metaheuristic approaches. Obtained numerical results show that metaheuristic algorithms can be successfully applied to solve the instances of this problem of up to 500 constraints. Among these four algorithms, the improved version of artificial bee algorithm is the most efficient with respect to the quality of the solution, robustness, and the computational efficiency.

Computer Control to Manage Risk of Pedestrian Head Crash Management Systems Based-High Risky Spots

2013 ◽  
Vol 321-324 ◽  
pp. 1982-1985
Author(s):  
Hong Ying Ji
Keyword(s):  
Objective Function ◽  
Shortest Path ◽  
Contact Area ◽  
Computer Control ◽  
Management Systems ◽  
Feasible Region ◽  
Forming Simulation ◽  
Drawing Direction ◽  
Optimal Direction ◽  
The Right

The right panel drawing direction is an important prerequisite for generating qualified parts, an important step before the panel forming simulation is to determine the reasonable direction of the drawing. Manually adjust parts in order to overcome rely on experience, the drawbacks to the drawing direction, the direction of the drawing punch and forming the contact area of the sheet as the goal of Computer Control determination Shortest Path Method. Objective function of the direction of the drawing for the variable contact area in the drawing direction of the feasible region, the use of heritage Shortest Path Methods to optimize the objective function of the contact area and, ultimately feasible within the contact area corresponding to the drawing direction, that is the best drawing direction. The measured results show that the direction of the drawing based on genetic Shortest Path Method, the Computer Control Shortest Path Method can fast and accurate to obtain the optimal direction of drawing.

scholarly journals Population Diversity Maintenance In Brain Storm Optimization Algorithm

2014 ◽  
Vol 4 (2) ◽  
pp. 83-97 ◽  
Author(s):  
Shi Cheng ◽  
Yuhui Shi ◽  
Quande Qin ◽  
Qingyu Zhang ◽  
Ruibin Bai
Keyword(s):  
Swarm Intelligence ◽  
Collective Behavior ◽  
Population Diversity ◽  
Premature Convergence ◽  
Exploration And Exploitation ◽  
Search Results ◽  
Brain Storm Optimization ◽  
Convergence And Divergence ◽  
Definition Of ◽  
The Brain

Abstract The convergence and divergence are two common phenomena in swarm intelligence. To obtain good search results, the algorithm should have a balance on convergence and divergence. The premature convergence happens partially due to the solutions getting clustered together, and not diverging again. The brain storm optimization (BSO), which is a young and promising algorithm in swarm intelligence, is based on the collective behavior of human being, that is, the brainstorming process. The convergence strategy is utilized in BSO algorithm to exploit search areas may contain good solutions. The new solutions are generated by divergence strategy to explore new search areas. Premature convergence also happens in the BSO algorithm. The solutions get clustered after a few iterations, which indicate that the population diversity decreases quickly during the search. A definition of population diversity in BSO algorithm is introduced in this paper to measure the change of solutions’ distribution. The algorithm's exploration and exploitation ability can be measured based on the change of population diversity. Different kinds of partial reinitialization strategies are utilized to improve the population diversity in BSO algorithm. The experimental results show that the performance of the BSO is improved by part of solutions re-initialization strategies.

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