scholarly journals A Concurrent Modelling to Generate Alternatives Approach Using the Firefly Algorithm

2013 ◽  
Vol 5 (2) ◽  
pp. 33-45 ◽  
Author(s):  
Raha Imanirad ◽  
Xin-She Yang ◽  
Julian Scott Yeomans
Keyword(s):  
System Performance ◽  
Firefly Algorithm ◽  
Multiple Solution ◽  
Performance Criteria ◽  
Objective Measures ◽  
Computationally Efficient ◽  
New Approach ◽  
Performance Requirements ◽  
Decision Variables ◽  
Performance Specifications

Real world” decision-making applications generally contain multifaceted performance requirements riddled with incongruent performance specifications. There are invariably unmodelled elements, not apparent during model construction, which can greatly impact the acceptability of the model’s solutions. Consequently, it is preferable to generate numerous alternatives that provide dissimilar approaches to the problem. These alternatives should possess near-optimal objective measures with respect to all known objective(s), but be maximally different from each other in terms of their decision variables. This maximally different solution creation approach is referred to as modelling-to-generate-alternatives (MGA). This study demonstrates how the Firefly Algorithm can concurrently create multiple solution alternatives that both satisfy required system performance criteria and yet are maximally different in their decision spaces. This new approach is computationally efficient, since it permits the concurrent generation of multiple, good solution alternatives in a single computational run rather than the multiple implementations required in previous MGA procedures.

Bio-Inspired Modelling to Generate Alternatives

2014 ◽  
pp. 361-371 ◽  
Author(s):  
Raha Imanirad ◽  
Julian Scott Yeomans
Keyword(s):  
Decision Making ◽  
Mathematical Programming ◽  
System Performance ◽  
Firefly Algorithm ◽  
Multiple Solution ◽  
Performance Criteria ◽  
Objective Measures ◽  
Solution Approach ◽  
Decision Variables ◽  
Design Requirements

“Real world” decision-making often involves complex problems that are riddled with incompatible and inconsistent performance objectives. These problems typically possess competing design requirements which are very difficult – if not impossible – to capture and quantify at the time that any supporting decision models are constructed. There are invariably unmodelled design issues, not apparent during the time of model construction, which can greatly impact the acceptability of the model's solutions. Consequently, when solving many practical mathematical programming applications, it is generally preferable to formulate numerous quantifiably good alternatives that provide very different perspectives to the problem. These alternatives should possess near-optimal objective measures with respect to all known modelled objectives, but be fundamentally different from each other in terms of the system structures characterized by their decision variables. This solution approach is referred to as modelling-to-generate-alternatives (MGA). This study demonstrates how the nature-inspired, Firefly Algorithm can be used to efficiently create multiple solution alternatives that both satisfy required system performance criteria and yet are maximally different in their decision spaces.

Simultaneous Modelling-to-Generate-Alternatives Procedure Employing the Firefly Algorithm

2019 ◽  
pp. 19-33
Author(s):  
Julian Scott Yeomans
Keyword(s):  
Decision Making ◽  
Firefly Algorithm ◽  
Objective Measures ◽  
Programming Approach ◽  
Benchmark Problem ◽  
Performance Requirements ◽  
Decision Variables ◽  
Performance Specifications ◽  
Mathematical Programming Approach ◽  
Design Requirements

“Real-world” decision-making applications generally contain multifaceted performance requirements riddled with incongruent performance specifications. This is because decision making typically involves complex problems that are riddled with incompatible performance objectives and contain competing design requirements which are very difficult—if not impossible—to capture and quantify at the time that the supporting decision models are actually constructed. There are invariably unmodelled elements, not apparent during model construction, which can greatly impact the acceptability of the model's solutions. Consequently, it is preferable to generate several distinct alternatives that provide multiple disparate perspectives to the problem. These alternatives should possess near-optimal objective measures with respect to all known objective(s), but be maximally different from each other in terms of their decision variables. This maximally different solution creation approach is referred to as modelling-to-generate-alternatives (MGA). This chapter provides an efficient optimization algorithm that simultaneously generates multiple, maximally different alternatives by employing the metaheuristic firefly algorithm. The efficacy of this mathematical programming approach is demonstrated on a commonly tested engineering optimization benchmark problem.

A Nature-Inspired Metaheuristic Approach for Generating Alternatives

2019 ◽  
pp. 722-733
Author(s):  
Julian Scott Yeomans
Keyword(s):  
Decision Making ◽  
Mathematical Programming ◽  
Real World ◽  
System Performance ◽  
Firefly Algorithm ◽  
Multiple Solution ◽  
Performance Criteria ◽  
Design Issues ◽  
Solution Approach ◽  
Design Requirements

“Real-world” decision making often involves complex problems that are riddled with incompatible and inconsistent performance objectives. These problems typically possess competing design requirements which are very difficult—if not impossible—to quantify and capture at the time that any supporting decision models are constructed. There are invariably unmodeled design issues, not apparent during the time of model construction, which can greatly impact the acceptability of the model's solutions. Consequently, when solving many practical mathematical programming applications, it is generally preferable to formulate numerous quantifiably good alternatives that provide very different perspectives to the problem. This solution approach is referred to as modelling to generate alternatives (MGA). This study demonstrates how the nature-inspired firefly algorithm can be used to efficiently create multiple solution alternatives that both satisfy required system performance criteria and yet are maximally different in their decision spaces.

A Nature-Inspired Metaheuristic Approach for Generating Alternatives

2018 ◽  
pp. 2178-2187 ◽  
Author(s):  
Julian Scott Yeomans
Keyword(s):  
Decision Making ◽  
Mathematical Programming ◽  
Real World ◽  
System Performance ◽  
Firefly Algorithm ◽  
Multiple Solution ◽  
Performance Criteria ◽  
Design Issues ◽  
Solution Approach ◽  
Design Requirements

“Real world” decision-making often involves complex problems that are riddled with incompatible and inconsistent performance objectives. These problems typically possess competing design requirements which are very difficult – if not impossible – to quantify and capture at the time that any supporting decision models are constructed. There are invariably unmodelled design issues, not apparent during the time of model construction, which can greatly impact the acceptability of the model's solutions. Consequently, when solving many practical mathematical programming applications, it is generally preferable to formulate numerous quantifiably good alternatives that provide very different perspectives to the problem. This solution approach is referred to as modelling-to-generate-alternatives (MGA). This study demonstrates how the nature-inspired, Firefly Algorithm can be used to efficiently create multiple solution alternatives that both satisfy required system performance criteria and yet are maximally different in their decision spaces.

Alternative Generation in Complex Decision Modelling Using a Firefly Algorithm Metaheuristic Approach

2020 ◽  
Vol 4 (2) ◽  
pp. 68-79
Author(s):  
Julian Scott Yeomans
Keyword(s):  
Decision Making ◽  
Firefly Algorithm ◽  
Programming Models ◽  
Objective Measures ◽  
Benchmark Problem ◽  
Biologically Inspired ◽  
Performance Requirements ◽  
Decision Variables ◽  
Alternative Generation ◽  
Complex Decision

Decision-making in the “real world” can become dominated by inconsistent performance requirements and incompatible specifications that can be difficult to detect when supporting mathematical programming models are formulated. There are invariably unmodelled elements, not apparent during model construction, which can greatly impact the acceptability of the model's solutions. Consequently, it can frequently prove beneficial to construct a set of options that provide dissimilar approaches to such problems. These alternatives should possess near-optimal objective measures with respect to all known objectives, but be maximally different from each other in terms of their decision variables. The approach for creating maximally different sets of solutions is referred to as modelling-to-generate-alternatives (MGA). This article provides an efficient biologically-inspired algorithm that can generate sets of maximally different alternatives by employing the Firefly Algorithm metaheuristic. The computational efficacy of this MGA approach is demonstrated on a commonly-tested benchmark problem.

A Biologically-Inspired Metaheuristic Approach for the Simultaneous Generation of Alternatives

2018 ◽  
Vol 3 (2) ◽  
pp. 1-12 ◽  
Author(s):  
Julian Scott Yeomans
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Test Problem ◽  
Firefly Algorithm ◽  
Objective Measures ◽  
Engineering Optimization ◽  
Biologically Inspired ◽  
Simultaneous Generation ◽  
Decision Variables ◽  
Decision Support Models

Decision-making in the “real world” involves complex problems that tend to be riddled with competing performance objectives and possess requirements which are very difficult to incorporate into any underlying decision support models. There are invariably unmodelled elements, not apparent during model construction, which can greatly impact the acceptability of the model's solutions. Consequently, it is preferable to generate numerous dissimilar alternatives that provide disparate perspectives to the problem. These alternatives should possess near-optimal objective measures with respect to all known objectives, but be maximally different from each other in terms of their decision variables. This maximally different solution creation approach is referred to as modelling-to-generate-alternatives (MGA). This article provides an efficient biologically-inspired algorithm that simultaneously generates multiple, maximally different alternatives by employing the Firefly Algorithm metaheuristic. The effectiveness of this algorithm is demonstrated on an engineering optimization benchmark test problem

System Requirements Development Methodology

1987 ◽  
Vol 31 (6) ◽  
pp. 625-628
Author(s):  
Joseph Conroy
Keyword(s):  
Environmental Conditions ◽  
System Performance ◽  
Decision Rules ◽  
Performance Criteria ◽  
Performance Requirements ◽  
Development Methodology ◽  
System Requirements ◽  
Cognitive Decision ◽  
Requirements Development ◽  
Computer Processor

This concept paper describes a computer processor which assists users in developing performance requirements for new systems. The output of the processor is function based objectives, performance based system criteria and environmental conditions which may effect system performance. The system criteria are based on the cognitive decision rules of experts for assessing the effectiveness of system performance. These are obtained from a regression based technique in which the experts rate the effectiveness of a system several times, each time with different values for each of the several candidate system performance criteria.

A new approach using a Binary Firefly Algorithm for the Set Covering Problem

2014 ◽  
Author(s):  
Broderick Crawford ◽  
Ricardo Soto ◽  
Miguel Olivares-Suárez ◽  
Fernando Paredes
Keyword(s):  
Firefly Algorithm ◽  
Set Covering ◽  
Covering Problem ◽  
Set Covering Problem ◽  
New Approach

scholarly journals Design framework for a seamless smart glove using a digital knitting system

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Yewon Song ◽  
Seulah Lee ◽  
Yuna Choi ◽  
Sora Han ◽  
Hyuna Won ◽  
...  
Keyword(s):  
Systematic Approach ◽  
Performance Criteria ◽  
Wearable Electronics ◽  
Design Development ◽  
Performance Requirements ◽  
Development Framework ◽  
Promising Strategy ◽  
Cad Cam ◽  
Wearable Electronic ◽  
Electronic Textile

AbstractThe wearable electronics integrated with textile-based devices is a promising strategy to meet the requirements of human comfort as well as electrical performances. This research presents a design and development framework for a seamless glove sensor system using digital knitting fabrication. Based on the performance requirements of glove sensors for controlling a prosthetic hand, desirable design components include electrical conductivity, comfort, formfit, electrical sensitivity, and customizable design. These attributes are determined and achieved by applying appropriate materials and fabrication technologies. In this study, a digital knitting CAD/CAM system is utilized to meet the desired performance criteria, and two prototypes of the seamless glove sensor systems are successfully developed for the detection of both human and robotic finger motions. This digital knitting system will provide considerable potential for customized design development as well as a sustainable production process. This structured, systematic approach could be adapted in the future development of wearable electronic textile systems.

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