scholarly journals A Swarm Optimization Algorithm for Multimodal Functions and Its Application in Multicircle Detection

2013 ◽  
Vol 2013 ◽  
pp. 1-22 ◽  
Author(s):  
Erik Cuevas ◽  
Daniel Zaldívar ◽  
Marco Pérez-Cisneros
Keyword(s):  
Animal Behavior ◽  
Optimization Algorithm ◽  
Engineering Problem ◽  
Migration Routes ◽  
Collective Behaviors ◽  
Cost Constraints ◽  
Engineering Problems ◽  
Biological Laws ◽  
Harvesting Efficiency ◽  
Evolutionary Operators

In engineering problems due to physical and cost constraints, the best results, obtained by a global optimization algorithm, cannot be realized always. Under such conditions, if multiple solutions (local and global) are known, the implementation can be quickly switched to another solution without much interrupting the design process. This paper presents a new swarm multimodal optimization algorithm named as the collective animal behavior (CAB). Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central location, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, searcher agents emulate a group of animals which interact with each other based on simple biological laws that are modeled as evolutionary operators. Numerical experiments are conducted to compare the proposed method with the state-of-the-art methods on benchmark functions. The proposed algorithm has been also applied to the engineering problem of multi-circle detection, achieving satisfactory results.

scholarly journals An Algorithm for Global Optimization Inspired by Collective Animal Behavior

2012 ◽  
Vol 2012 ◽  
pp. 1-24 ◽  
Author(s):  
Erik Cuevas ◽  
Mauricio González ◽  
Daniel Zaldivar ◽  
Marco Pérez-Cisneros ◽  
Guillermo García
Keyword(s):  
Global Optimization ◽  
Animal Behavior ◽  
Food Source ◽  
Collective Motion ◽  
Global Optimum ◽  
Migration Routes ◽  
Collective Behaviors ◽  
Biological Laws ◽  
Harvesting Efficiency ◽  
Collective Animal Behavior

A metaheuristic algorithm for global optimization called the collective animal behavior (CAB) is introduced. Animal groups, such as schools of fish, flocks of birds, swarms of locusts, and herds of wildebeest, exhibit a variety of behaviors including swarming about a food source, milling around a central locations, or migrating over large distances in aligned groups. These collective behaviors are often advantageous to groups, allowing them to increase their harvesting efficiency, to follow better migration routes, to improve their aerodynamic, and to avoid predation. In the proposed algorithm, the searcher agents emulate a group of animals which interact with each other based on the biological laws of collective motion. The proposed method has been compared to other well-known optimization algorithms. The results show good performance of the proposed method when searching for a global optimum of several benchmark functions.

scholarly journals Buyer Inspired Meta-Heuristic Optimization Algorithm

2020 ◽  
Vol 10 (1) ◽  
pp. 194-219 ◽  
Author(s):  
Sanjoy Debnath ◽  
Wasim Arif ◽  
Srimanta Baishya
Keyword(s):  
Optimization Algorithm ◽  
Optimization Technique ◽  
Statistical Test ◽  
Heuristic Optimization ◽  
Product Choice ◽  
Exploration And Exploitation ◽  
The Social ◽  
Engineering Problems ◽  
The Mean

AbstractNature inspired swarm based meta-heuristic optimization technique is getting considerable attention and established to be very competitive with evolution based and physical based algorithms. This paper proposes a novel Buyer Inspired Meta-heuristic optimization Algorithm (BIMA) inspired form the social behaviour of human being in searching and bargaining for products. In BIMA, exploration and exploitation are achieved through shop to shop hoping and bargaining for products to be purchased based on cost, quality of the product, choice and distance to the shop. Comprehensive simulations are performed on 23 standard mathematical and CEC2017 benchmark functions and 3 engineering problems. An exhaustive comparative analysis with other algorithms is done by performing 30 independent runs and comparing the mean, standard deviation as well as by performing statistical test. The results showed significant improvement in terms of optimum value, convergence speed, and is also statistically more significant in comparison to most of the reported popular algorithms.

Model-Eliciting Activities: A Research-Based Method for Inquiry Learning and Professional Development in the Engineering Classroom

2008 ◽  
Author(s):  
Tamara J. Moore
Keyword(s):  
Complex Systems ◽  
Student Development ◽  
Engineering Problem ◽  
First Year ◽  
Foundation Engineering ◽  
Underrepresented Populations ◽  
Model Eliciting Activities ◽  
Engineering Problems ◽  
The Given ◽  
Mathematics And Science

Attracting students to engineering is a challenge. In addition, ABET requires that engineering graduates be able to work on multi-disciplinary teams and apply mathematics and science when solving engineering problems. One manner of integrating teamwork and engineering contexts in a first-year foundation engineering course is through the use of Model-Eliciting Activities (MEAs) — realistic, client-driven problems based on the models and modeling theoretical framework. A Model-Eliciting Activity (MEA) is a real-world client-driven problem. The solution of an MEA requires the use of one or more mathematical or engineering concepts that are unspecified by the problem — students must make new sense of their existing knowledge and understandings to formulate a generalizable mathematical model that can be used by the client to solve the given and similar problems. An MEA creates an environment in which skills beyond mathematical abilities are valued because the focus is not on the use of prescribed equations and algorithms but on the use of a broader spectrum of skills required for effective engineering problem-solving. Carefully constructed MEAs can begin to prepare students to communicate and work effectively in teams; to adopt and adapt conceptual tools; to construct, describe, and explain complex systems; and to cope with complex systems. MEAs provide a learning environment that is tailored to a more diverse population than typical engineering course experiences as they allow students with different backgrounds and values to emerge as talented, and that adapting these types of activities to engineering courses has the potential to go beyond “filling the gaps” to “opening doors” to women and underrepresented populations in engineering. Further, MEAs provide evidence of student development in regards to ABET standards. Through NSF-funded grants, multiple MEAs have been developed and implemented with a MSE-flavored nanotechnology theme. This paper will focus on the content, implementation, and student results of one of these MEAs.

scholarly journals Optimization of Composite Fracture Properties: Method, Validation, and Applications

2016 ◽  
Vol 83 (7) ◽  
Author(s):  
Grace X. Gu ◽  
Leon Dimas ◽  
Zhao Qin ◽  
Markus J. Buehler
Keyword(s):  
Optimization Algorithm ◽  
Material Properties ◽  
Building Blocks ◽  
Test Case ◽  
Brute Force ◽  
Vast Number ◽  
Engineering Problems ◽  
Brute Force Method ◽  
Optimization Parameters ◽  
Underlying Mechanisms

A paradigm in nature is to architect composites with excellent material properties compared to its constituents, which themselves often have contrasting mechanical behavior. Most engineering materials sacrifice strength for toughness, whereas natural materials do not face this tradeoff. However, biology's designs, adapted for organism survival, may have features not needed for some engineering applications. Here, we postulate that mimicking nature's elegant use of multimaterial phases can lead to better optimization of engineered materials. We employ an optimization algorithm to explore and design composites using soft and stiff building blocks to study the underlying mechanisms of nature's tough materials. For different applications, optimization parameters may vary. Validation of the algorithm is carried out using a test suite of cases without cracks to optimize for stiffness and compliance individually. A test case with a crack is also performed to optimize for toughness. The validation shows excellent agreement between geometries obtained from the optimization algorithm and the brute force method. This study uses different objective functions to optimize toughness, stiffness and toughness, and compliance and toughness. The algorithm presented here can provide researchers a way to tune material properties for a vast number of engineering problems by adjusting the distribution of soft and stiff materials.

scholarly journals SOME STATISTICAL TECHNIQUES APPLIED TO ENGINEERING MECHANICS PROBLEMS

2020 ◽  
Vol 57 (6A) ◽  
pp. 10
Author(s):  
Tham Hong Duong
Keyword(s):  
Taguchi Method ◽  
Input Data ◽  
Technical Specification ◽  
Engineering Problem ◽  
Statistical Techniques ◽  
Test Cases ◽  
Statistical Tools ◽  
Technology Process ◽  
Engineering Problems ◽  
Engineering Mechanics

This article deals with statistical techniques normally used in Engineering. Variables or parameters in models of Engineering Mechanics always face data:  a) of materials (with technical specification); b) of analysing model using specific software; c) of measurement using variety of devices and approaches; and d) of the technology process of manufacture (outcome). An engineering object to be studied has k variables and each variable has m values or level of status, it will need mk cases to be solved. This has to conduct a very large number of test cases to be solved for target objective(s). A Taguchi Method will be applied for finding solution in which much less effort of computation is paid and other different conditions of noise could be taken into account. Besides, other statistical tools, ANOVA have also proved to be useful in quantifying uncertainties in engineering problems, both in aleatory (nature) and epistemic (knowledge and measurement) categories. A typical example of engineering problem is chosen to study using above-mentioned Taguchi method and statistical tools. This method is very useful for design of experiments, both in traditional laboratory and computer numerical modeling and it can used to optimize the set of input data for obtaining the best results of outcome product.

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