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.

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 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.

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.

A Stochastic Graph Grammar Algorithm for Interactive Search

2009 ◽  
Author(s):  
Matthew I. Campbell ◽  
Rahul Rai ◽  
Tolga Kurtoglu
Keyword(s):  
Conceptual Design ◽  
User Interaction ◽  
Generative Grammar ◽  
Search Method ◽  
Graph Grammar ◽  
Decision Making Process ◽  
Design Decisions ◽  
Interactive Search ◽  
Stochastic Graph ◽  
Single Configuration

This paper presents a new search method that has been developed specifically for search trees defined by a generative grammar. Generative grammars are useful in design as a way to encapsulate the design decisions that lead to candidate solutions. Since the candidate solutions are not confined to a single configuration or topology and thus useful in conceptual design, they may be difficult to computationally analyze. Analysis is achieved in this method by querying the user. The user interaction is kept to a maximum of thirty pair-wise comparisons of candidates. From the data gathered from the comparisons, a stochastic decision making process infers what candidate solutions best meet the user’s preference. The method is implemented and applied to a grammar for tying neckties. It is shown through 21 user experiments and 4000 automated experiments that the method consistently finds solutions within the 99.8 percentile. The implications of this method for conceptual design are expounded on in the conclusions.

Localization of WSN using Distributed Particle Swarm Optimization algorithm with precise references

2016 ◽  
Vol 7 ◽  
pp. 1
Author(s):  
Ravichander Janapati ◽  
Ch. Balaswamy ◽  
K. Soundararajan
Keyword(s):  
Particle Swarm Optimization ◽  
Search Algorithm ◽  
Particle Swarm ◽  
Search Space ◽  
Research Area ◽  
Population Based ◽  
Wireless Sensor ◽  
Cooperative Localization ◽  
Localization Algorithm ◽  
Swarm Optimization

Localization is the key research area in wireless sensor networks. Finding the exact position of the node is known as localization. Different algorithms have been proposed. Here we consider a cooperative localization algorithm with censoring schemes using Crammer Rao bound (CRB). This censoring scheme  can improve the positioning accuracy and reduces computation complexity, traffic and latency. Particle swarm optimization (PSO) is a population based search algorithm based on the swarm intelligence like social behavior of birds, bees or a school of fishes. To improve the algorithm efficiency and localization precision, this paper presents an objective function based on the normal distribution of ranging error and a method of obtaining the search space of particles. In this paper  Distributed localization of wireless sensor networksis proposed using PSO with best censoring technique using CRB. Proposed method shows better results in terms of position accuracy, latency and complexity.  

scholarly journals Binary Spring Search Algorithm for Solving Various Optimization Problems

2021 ◽  
Vol 11 (3) ◽  
pp. 1286 ◽  
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Ali Dehghani ◽  
Om P. Malik ◽  
Ruben Morales-Menendez ◽  
...  
Keyword(s):  
Optimization Algorithm ◽  
High Performance ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Bat Algorithm ◽  
Search Space ◽  
Binary Particle Swarm Optimization ◽  
Dragonfly Algorithm ◽  
Grasshopper Optimization Algorithm

One of the most powerful tools for solving optimization problems is optimization algorithms (inspired by nature) based on populations. These algorithms provide a solution to a problem by randomly searching in the search space. The design’s central idea is derived from various natural phenomena, the behavior and living conditions of living organisms, laws of physics, etc. A new population-based optimization algorithm called the Binary Spring Search Algorithm (BSSA) is introduced to solve optimization problems. BSSA is an algorithm based on a simulation of the famous Hooke’s law (physics) for the traditional weights and springs system. In this proposal, the population comprises weights that are connected by unique springs. The mathematical modeling of the proposed algorithm is presented to be used to achieve solutions to optimization problems. The results were thoroughly validated in different unimodal and multimodal functions; additionally, the BSSA was compared with high-performance algorithms: binary grasshopper optimization algorithm, binary dragonfly algorithm, binary bat algorithm, binary gravitational search algorithm, binary particle swarm optimization, and binary genetic algorithm. The results show the superiority of the BSSA. The results of the Friedman test corroborate that the BSSA is more competitive.

scholarly journals Fast Search Method Based on Vector Quantization for Raman Spectroscopy Identification

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1970
Author(s):  
Jun-Kyu Park ◽  
Suwoong Lee ◽  
Aaron Park ◽  
Sung-June Baek
Keyword(s):  
Search Algorithm ◽  
Principal Component ◽  
Search Method ◽  
Reference Spectrum ◽  
Search Range ◽  
Fast Search ◽  
Input Spectrum ◽  
Computationally Intensive ◽  
Multiple Clusters ◽  
Full Search

In spectroscopy, matching a measured spectrum to a reference spectrum in a large database is often computationally intensive. To solve this problem, we propose a novel fast search algorithm that finds the most similar spectrum in the database. The proposed method is based on principal component transformation and provides results equivalent to the traditional full search method. To reduce the search range, hierarchical clustering is employed, which divides the spectral data into multiple clusters according to the similarity of the spectrum, allowing the search to start at the cluster closest to the input spectrum. Furthermore, a pilot search was applied in advance to further accelerate the search. Experimental results show that the proposed method requires only a small fraction of the computational complexity required by the full search, and it outperforms the previous methods.

How significant are the known collision and element distinctness quantum algorithms?

2004 ◽  
Vol 4 (3) ◽  
pp. 201-206
Author(s):  
L. Grover ◽  
T. Rudolph
Keyword(s):  
Search Algorithm ◽  
Quantum Algorithms ◽  
Search Space ◽  
Space Complexity ◽  
Quantum Search ◽  
Quantum Search Algorithm ◽  
Time Space ◽  
Simple Quantum ◽  
Structured Problems ◽  
Better Than

Quantum search is a technique for searching $N$ possibilities for a desired target in $O(\sqrt{N})$ steps. It has been applied in the design of quantum algorithms for several structured problems. Many of these algorithms require significant amount of quantum hardware. In this paper we propose the criterion that an algorithm which requires $O(S)$ hardware should be considered significant if it produces a speedup of better than $O\left(\sqrt{S}\right)$ over a simple quantum search algorithm. This is because a speedup of $O\left(\sqrt{S}\right)$ can be trivially obtained by dividing the search space into $S$ separate parts and handing the problem to $S$ independent processors that do a quantum search (in this paper we drop all logarithmic factors when discussing time/space complexity). Known algorithms for collision and element distinctness exactly saturate the criterion.

An Interactive Visualization of Genetic Algorithm on 2-D Graph

2012 ◽  
Vol 4 (1) ◽  
pp. 34-54 ◽  
Author(s):  
Humera Farooq ◽  
Nordin Zakaria ◽  
Muhammad Tariq Siddique
Keyword(s):  
Genetic Algorithm ◽  
Growth Process ◽  
Search Space ◽  
Human Intervention ◽  
Branching Structures ◽  
Multidimensional Search ◽  
L System ◽  
User Fatigue ◽  
Search Data ◽  
Selection Of

The visualization of search space makes it easy to understand the behavior of the Genetic Algorithm (GA). The authors propose a novel way for representation of multidimensional search space of the GA using 2-D graph. This is carried out based on the gene values of the current generation, and human intervention is only required after several generations. The main contribution of this research is to propose an approach to visualize the GA search data and improve the searching process of the GA with human’s intention in different generations. Besides the selection of best individual or parents for the next generation, interference of human is required to propose a new individual in the search space. Active human intervention leads to a faster searching, resulting in less user fatigue. The experiments were carried out by evolving the parameters to derive the rules for a Parametric L-System. These rules are then used to model the growth process of branching structures in 3-D space. The experiments were conducted to evaluate the ability of the proposed approach to converge to optimized solution as compared to the Simple Genetic Algorithm (SGA).

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