scholarly journals An Improved Squirrel Search Algorithm for Global Function Optimization

Algorithms ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 80 ◽  
Author(s):  
Yanjiao Wang ◽  
Tianlin Du
Keyword(s):  
Search Algorithm ◽  
Statistical Tests ◽  
Search Space ◽  
Search Method ◽  
Population Diversity ◽  
Convergence Speed ◽  
Function Optimization ◽  
Experimental Results ◽  
Selection Strategy ◽  
Evolutionary Information

An improved squirrel search algorithm (ISSA) is proposed in this paper. The proposed algorithm contains two searching methods, one is the jumping search method, and the other is the progressive search method. The practical method used in the evolutionary process is selected automatically through the linear regression selection strategy, which enhances the robustness of squirrel search algorithm (SSA). For the jumping search method, the ‘escape’ operation develops the search space sufficiently and the ‘death’ operation further explores the developed space, which balances the development and exploration ability of SSA. Concerning the progressive search method, the mutation operation fully preserves the current evolutionary information and pays more attention to maintain the population diversity. Twenty-one benchmark functions are selected to test the performance of ISSA. The experimental results show that the proposed algorithm can improve the convergence accuracy, accelerate the convergence speed as well as maintain the population diversity. The statistical test proves that ISSA has significant advantages compared with SSA. Furthermore, compared with five other intelligence evolutionary algorithms, the experimental results and statistical tests also show that ISSA has obvious advantages on convergence accuracy, convergence speed and robustness.

Automatic Mining of Quantitative Association Rules with Gravitational Search Algorithm

2017 ◽  
Vol 27 (03) ◽  
pp. 343-372 ◽  
Author(s):  
Umit Can ◽  
Bilal Alatas
Keyword(s):  
Association Rules ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Fitness Function ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Search Space ◽  
Search Method ◽  
Quantitative Association Rules ◽  
Gravitational Search

The classical optimization algorithms are not efficient in solving complex search and optimization problems. Thus, some heuristic optimization algorithms have been proposed. In this paper, exploration of association rules within numerical databases with Gravitational Search Algorithm (GSA) has been firstly performed. GSA has been designed as search method for quantitative association rules from the databases which can be regarded as search space. Furthermore, determining the minimum values of confidence and support for every database which is a hard job has been eliminated by GSA. Apart from this, the fitness function used for GSA is very flexible. According to the interested problem, some parameters can be removed from or added to the fitness function. The range values of the attributes have been automatically adjusted during the time of mining of the rules. That is why there is not any requirements for the pre-processing of the data. Attributes interaction problem has also been eliminated with the designed GSA. GSA has been tested with four real databases and promising results have been obtained. GSA seems an effective search method for complex numerical sequential patterns mining, numerical classification rules mining, and clustering rules mining tasks of data mining.

scholarly journals A bi-level optimization model for grouping constrained storage location assignment problems

2021 ◽  
Author(s):  
Jing Xie ◽  
Yi Mei ◽  
Andreas T Ernst ◽  
Xiaodong Li ◽  
Andy Song
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Random Search ◽  
Search Space ◽  
Search Method ◽  
Fitness Evaluation ◽  
Random Search Method ◽  
Storage Location ◽  
Location Assignment ◽  
Upper Level

In this paper, a novel bi-level grouping optimization (BIGO) model is proposed for solving the storage location assignment problem with grouping constraint (SLAP-GC). A major challenge in this problem is the grouping constraint which restricts the number of groups each product can have and the locations of items in the same group. In SLAP-GC, the problem consists of two subproblems, one is how to group the items, and the other one is how to assign the groups to locations. It is an arduous task to solve the two subproblems simultaneously. To overcome this difficulty, we propose a BIGO. BIGO optimizes item grouping in the upper level, and uses the lower-level optimization to evaluate each item grouping. Sophisticated fitness evaluation and search operators are designed for both upper and lower level optimization so that the feasibility of solutions can be guaranteed, and the search can focus on promising areas in the search space. Based on the BIGO model, a multistart random search method and a tabu search algorithm are proposed. The experimental results on the real-world dataset validate the efficacy of the BIGO model and the advantage of the tabu search method over the random search method. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

scholarly journals Population Diversity Control of Genetic Algorithm Using a Novel Injection Method for Bankruptcy Prediction Problem

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 823
Author(s):  
Nabeel Al-Milli ◽  
Amjad Hudaib ◽  
Nadim Obeid
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Search Algorithm ◽  
Search Space ◽  
Bankruptcy Prediction ◽  
Population Diversity ◽  
Exploration And Exploitation ◽  
Exploration Process ◽  
Injection Method ◽  
Population Based Algorithm

Exploration and exploitation are the two main concepts of success for searching algorithms. Controlling exploration and exploitation while executing the search algorithm will enhance the overall performance of the searching algorithm. Exploration and exploitation are usually controlled offline by proper settings of parameters that affect the population-based algorithm performance. In this paper, we proposed a dynamic controller for one of the most well-known search algorithms, which is the Genetic Algorithm (GA). Population Diversity Controller-GA (PDC-GA) is proposed as a novel feature-selection algorithm to reduce the search space while building a machine-learning classifier. The PDC-GA is proposed by combining GA with k-mean clustering to control population diversity through the exploration process. An injection method is proposed to redistribute the population once 90% of the solutions are located in one cluster. A real case study of a bankruptcy problem obtained from UCI Machine Learning Repository is used in this paper as a binary classification problem. The obtained results show the ability of the proposed approach to enhance the performance of the machine learning classifiers in the range of 1% to 4%.

scholarly journals A bi-level optimization model for grouping constrained storage location assignment problems

2021 ◽  
Author(s):  
Jing Xie ◽  
Yi Mei ◽  
Andreas T Ernst ◽  
Xiaodong Li ◽  
Andy Song
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Random Search ◽  
Search Space ◽  
Search Method ◽  
Fitness Evaluation ◽  
Random Search Method ◽  
Storage Location ◽  
Location Assignment ◽  
Upper Level

In this paper, a novel bi-level grouping optimization (BIGO) model is proposed for solving the storage location assignment problem with grouping constraint (SLAP-GC). A major challenge in this problem is the grouping constraint which restricts the number of groups each product can have and the locations of items in the same group. In SLAP-GC, the problem consists of two subproblems, one is how to group the items, and the other one is how to assign the groups to locations. It is an arduous task to solve the two subproblems simultaneously. To overcome this difficulty, we propose a BIGO. BIGO optimizes item grouping in the upper level, and uses the lower-level optimization to evaluate each item grouping. Sophisticated fitness evaluation and search operators are designed for both upper and lower level optimization so that the feasibility of solutions can be guaranteed, and the search can focus on promising areas in the search space. Based on the BIGO model, a multistart random search method and a tabu search algorithm are proposed. The experimental results on the real-world dataset validate the efficacy of the BIGO model and the advantage of the tabu search method over the random search method. © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

scholarly journals A Modified Artificial Bee Colony Algorithm Based on Search Space Division and Disruptive Selection Strategy

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Zhen-an He ◽  
Caiwen Ma ◽  
Xianhong Wang ◽  
Lei Li ◽  
Ying Wang ◽  
...  
Keyword(s):  
Artificial Bee Colony ◽  
Search Space ◽  
Poor Quality ◽  
Population Diversity ◽  
Disruptive Selection ◽  
Selection Strategy ◽  
Abc Algorithm ◽  
Space Division ◽  
Bee Colony

Artificial bee colony (ABC) algorithm has attracted much attention and has been applied to many scientific and engineering applications in recent years. However, there are still some insufficiencies in ABC algorithm such as poor quality of initial solution, slow convergence, premature, and low precision, which hamper the further development and application of ABC. In order to further improve the performance of ABC, we first proposed a novel initialization method called search space division (SSD), which provided high quality of initial solutions. And then, a disruptive selection strategy was used to improve population diversity. Moreover, in order to accelerate convergence rate, we changed the definition of the scout bee phase. In addition, we designed two types of experiments to testify our proposed algorithm. On the one hand, we conducted experiments to make sure how much each modification makes contribution to improving the performance of ABC. On the other hand, comprehensive experiments were performed to prove the superiority of our proposed algorithm. The experimental results indicate that SDABC significantly outperforms other ABCs, contributing to higher solution accuracy, faster convergence speed, and stronger algorithm stability.

scholarly journals Enhancing Backtracking Search Algorithm using Reflection Mutation Strategy Based on Sine Cosine

Algorithms ◽  
2019 ◽  
Vol 12 (11) ◽  
pp. 225
Author(s):  
Chong Zhou ◽  
Shengjie Li ◽  
Yuhe Zhang ◽  
Zhikun Chen ◽  
Cuijun Zhang
Keyword(s):  
Heuristic Algorithms ◽  
Search Algorithm ◽  
Population Based ◽  
Population Diversity ◽  
Convergence Speed ◽  
Backtracking Search ◽  
Backtracking Search Algorithm ◽  
Current Population ◽  
Unit Simplex ◽  
Mutation Strategy

Backtracking Search Algorithm (BSA) is a younger population-based evolutionary algorithm and widely researched. Due to the introduction of historical population and no guidance toward to the best individual, BSA does not adequately use the information in the current population, which leads to a slow convergence speed and poor exploitation ability of BSA. To address these drawbacks, a novel backtracking search algorithm with reflection mutation based on sine cosine is proposed, named RSCBSA. The best individual found so far is employed to improve convergence speed, while sine and cosine math models are introduced to enhance population diversity. To sufficiently use the information in the historical population and current population, four individuals are selected from the historical or current population randomly to construct an unit simplex, and the center of the unit simplex can enhance exploitation ability of RSCBSA. Comprehensive experimental results and analyses show that RSCBSA is competitive enough with other state-of-the-art meta-heuristic algorithms.

scholarly journals Collaborative Target Search Algorithm for UAV Based on Chaotic Disturbance Pigeon-Inspired Optimization

2021 ◽  
Vol 11 (16) ◽  
pp. 7358
Author(s):  
Linlin Li ◽  
Shufang Xu ◽  
Hua Nie ◽  
Yingchi Mao ◽  
Shun Yu
Keyword(s):  
Optimization Algorithm ◽  
Search Algorithm ◽  
Chaotic Map ◽  
Poor Performance ◽  
Population Diversity ◽  
Convergence Speed ◽  
Local Optimum ◽  
Search Problem ◽  
Target Search ◽  
Disturbance Factor

Unmanned aerial vehicles (UAVs) have shown their superiority in military and civilian missions. In the face of complex tasks, many UAVs are usually needed to cooperate with each other. Therefore, multi-UAV cooperative target search has attracted more and more scholars’ attention. At present, there are many bionic algorithms for solving the cooperative search problem of multi-UAVs, including particle swarm optimization algorithm (PSO) and differential evolution (DE). Pigeon-inspired optimization (PIO) is a new swarm intelligence optimization algorithm proposed in recent years. It has great advantages over other algorithms in convergence, robustness, and accuracy, and has few parameters to be adjusted. Aiming at the shortcomings of the standard pigeon colony algorithm, such as poor population diversity, slow convergence speed, and the ease of falling into local optimum, we have proposed chaotic disturbance pigeon-inspired optimization (CDPIO) algorithm. The improved tent chaotic map was used to initialize the population and increase the diversity of the population. The disturbance factor is introduced in the iterative update stage of the algorithm to generate new individuals, replace the individuals with poor performance, and carry out disturbance to increase the optimization accuracy. Benchmark functions and UAV target search model were used to test the algorithm performance. The results show that the CDPIO had faster convergence speed, better optimization precision, better robustness, and better performance than PIO.

scholarly journals Parallel repulsive logic regression with biological adjacency

Biostatistics ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 825-844
Author(s):  
Daisuke Yoneoka ◽  
Cindy Im ◽  
Yutaka Yasui
Keyword(s):  
Objective Function ◽  
Search Algorithm ◽  
Association Studies ◽  
Search Space ◽  
Function Optimization ◽  
Detection Accuracy ◽  
Genome Wide Association Studies ◽  
Large Space ◽  
Logic Regression ◽  
Repulsive Forces

Summary Logic regression, an extension of generalized linear models with Boolean combinations of binary variables as predictors, is a useful tool in exploring interactions among single-nucleotide polymorphisms (SNPs) in genome-wide association studies. However, since the search space defined by all possible combinations of SNPs, their complements, and logical operators in Boolean expressions can be exceedingly large in such studies, objective function optimization is slow and likely to be trapped in many local solutions, resulting in model over-fitting. We introduce a new search algorithm, parallel repulsive logic regression (PRLR), to efficiently estimate parameters of a logic regression to find a best model within the large space of SNP interactions by incorporating: (i) relevant biological adjacency matrix between SNPs to define similarity of estimation paths or trees, which are derived from physical SNP positions on chromosomes and/or memberships in biological gene pathways; and (ii) two repulsive forces to counter the similarity between and within estimation paths considered in parallel, which are introduced as penalty terms in the objective function. We compare our method’s performance for identifying biologically-meaningful SNP interactions through simulations and with real genetic-epidemiological data. PRLR’s detection-accuracy measures outperform existing approaches, especially in terms of positive predictive value and sensitivity for detecting SNP–SNP interactions.

scholarly journals A Cuckoo Search Algorithm for Multimodal Optimization

2014 ◽  
Vol 2014 ◽  
pp. 1-20 ◽  
Author(s):  
Erik Cuevas ◽  
Adolfo Reyna-Orta
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Search Space ◽  
Selection Strategy ◽  
Test Problems ◽  
Multimodal Optimization ◽  
Local Optima ◽  
Fitness Value

Interest in multimodal optimization is expanding rapidly, since many practical engineering problems demand the localization of multiple optima within a search space. On the other hand, the cuckoo search (CS) algorithm is a simple and effective global optimization algorithm which can not be directly applied to solve multimodal optimization problems. This paper proposes a new multimodal optimization algorithm called the multimodal cuckoo search (MCS). Under MCS, the original CS is enhanced with multimodal capacities by means of (1) the incorporation of a memory mechanism to efficiently register potential local optima according to their fitness value and the distance to other potential solutions, (2) the modification of the original CS individual selection strategy to accelerate the detection process of new local minima, and (3) the inclusion of a depuration procedure to cyclically eliminate duplicated memory elements. The performance of the proposed approach is compared to several state-of-the-art multimodal optimization algorithms considering a benchmark suite of fourteen multimodal problems. Experimental results indicate that the proposed strategy is capable of providing better and even a more consistent performance over existing well-known multimodal algorithms for the majority of test problems yet avoiding any serious computational deterioration.

A Novel Search Algorithm for Interactive Automated Conceptual Design Generator (ACDG)

2011 ◽  
Author(s):  
Rahul Rai ◽  
Pranay Kilaru ◽  
Ravi Vallepalli ◽  
Matthew I. Campbell
Keyword(s):  
Conceptual Design ◽  
Search Algorithm ◽  
Generative Grammar ◽  
Search Space ◽  
Search Method ◽  
Concept Generation ◽  
Formal Representation ◽  
Human In The Loop ◽  
Multiple User ◽  
User Fatigue

Automated concept generation is non-trivial task. The complexity of this problem is mainly due to lack of formal representation frameworks that lend themselves easily to a computational approach. Generative grammar has emerged as a potential solution to this problem and presents a number of different possibilities for conceptual design automation. This paper presents a novel search method that has been developed specifically for search trees defined by a special class of generative grammar in which rules of the grammar have parameters associated with them. A novel feature of the proposed search is ‘Human in the loop’ approach in which learning about the search space is achieved by querying the user. The user fatigue restricts the maximum number of comparisons of candidate solutions (30–50). From the data gathered from the comparisons, a stochastic decision making process proposed in this paper quickly converges to a region of design space which best meet the user’s preference. The method is implemented and applied to a grammar for shampoo bottle concept generation. It is shown through multiple user-guided and automated experiments that the method has ability to learn and adopt through human computer interaction process. The implications of the proposed search method for automated conceptual design are expounded on in the conclusions.

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