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

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.

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.

Models of decision support in socio-cyber-physical systems

2019 ◽  
pp. 55-70 ◽  
Author(s):  
A. V. Smirnov ◽  
T. V. Levashova
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Situation Awareness ◽  
Cyber Physical Systems ◽  
Emergent Behavior ◽  
Emergent Properties ◽  
Decision Making Process ◽  
Physical Systems ◽  
Decision Support Models ◽  
Common Goals

Introduction: Socio-cyber-physical systems are complex non-linear systems. Such systems display emergent properties. Involvement of humans, as a part of these systems, in the decision-making process contributes to overcoming the consequences of the emergent system behavior, since people can use their experience and intuition, not just the programmed rules and procedures.Purpose: Development of models for decision support in socio-cyber-physical systems.Results: A scheme of decision making in socio-cyber-physical systems, a conceptual framework of decision support in these systems, and stepwise decision support models have been developed. The decision-making scheme is that cybernetic components make their decisions first, and if they cannot do this, they ask humans for help. The stepwise models support the decisions made by components of socio-cyber-physical systems at the conventional stages of the decision-making process: situation awareness, problem identification, development of alternatives, choice of a preferred alternative, and decision implementation. The application of the developed models is illustrated through a scenario for planning the execution of a common task for robots.Practical relevance: The developed models enable you to design plans on solving tasks common for system components or on achievement of common goals, and to implement these plans. The models contribute to overcoming the consequences of the emergent behavior of socio-cyber-physical systems, and to the research on machine learning and mobile robot control.

Model of the Decision Support System under Condition of Non-Determination for Manufacturing Process Support

2014 ◽  
Vol 611 ◽  
pp. 416-423
Author(s):  
Cyril Klimeš ◽  
Radim Farana
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Risk Analysis ◽  
Decision Support System ◽  
Decision Support Systems ◽  
Support System ◽  
Manufacturing Process ◽  
Uncertain Information ◽  
Process Support ◽  
Decision Support Models

Decision support systems mean interactive computer systems, which assist to decision making subjects to utilize both data and models to solve non-structured issues. These systems were established mainly on the basis of a risk analysis, utilizing the experience/skills, conclusion making and intuition, enabling very fast and flexible analysis with a good response, enabling the application of manager intuition and judgment this way. However, such decisions are often based on uncertain information. This fact requires the establishment of other decision support models.

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.

Policy-makers’ attitudes to decision support models for coronary heart disease: A qualitative study

2008 ◽  
Vol 13 (4) ◽  
pp. 209-214 ◽  
Author(s):  
David Taylor-Robinson ◽  
Beth Milton ◽  
Ffion Lloyd-Williams ◽  
Martin O'flaherty ◽  
Simon Capewell
Keyword(s):  
Decision Making ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Decision Support ◽  
Qualitative Study ◽  
Population Level ◽  
Policy Makers ◽  
Decision Support Models ◽  
Support Decision Making ◽  
Use Of Models

Objectives: To explore attitudes to the use of models for coronary heart disease to support decision-making for policy and service planning. Methods: Qualitative study using semi-structured interviews with 33 policy- and decision-makers purposively sampled from the UK National Health Service (NHS) (national, regional and local levels), academia and voluntary organizations. Interviews were transcribed, coded and emergent themes identified using framework analysis aided by NVivo software. Results: Policy-makers and planners were generally enthusiastic about models to assist in decision-making through: predicting trends; assessing the effect of interventions on health inequalities; quantifying the impact of population level and targeted interventions, and facilitating economic evaluation. The perceived advantages of using models included: more rational commissioning; the facility for scenario testing; advocacy for population level interventions and off-the-shelf synthesis to aid real time decision-making. However, although participants were aware of models to support decision-making, these were not being used routinely. Some participants felt that models oversimplify complex situations and that there is a lack of shared understanding as to how models work. Factors that increase confidence in decision support models included: rigorous validation and peer review, the availability of user-support and increased transparency. Conclusion: Policy-makers and planners were generally enthusiastic about the use of models to support decision-making, illustrating the potential uses for models and the factors that improve confidence in them. However, existing models are often not being used in practice. So new models that are fit for practice need to be developed.

Sistem Pendukung Keputusan SNMPTN Jalur Undangan Dengan Metode Electre

2018 ◽  
Vol 3 ◽  
pp. 14
Author(s):  
Lidia K Simanjuntak ◽  
Tessa Y M Sihite ◽  
Mesran Mesran ◽  
Nuning Kurniasih ◽  
Yuhandri Yuhandri
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Action Selection ◽  
Multi Criteria Decision Making ◽  
Prospective Students ◽  
College Entrance ◽  
Electre Method ◽  
Selection Of

All colleges each year organize the selection of new admissions. Acceptance of prospective students in universities as education providers is done by selecting prospective students based on achievement in school and college entrance selection. To select the best student candidates based on predetermined criteria, then use Multi-Criteria Decision Making (MCDM) or commonly called decision support system. One method in MCDM is the Elimination Et Choix Traduisant la Reality (ELECTRE). The ELECTRE method is the best method of action selection. The ELECTRE method to obtain the best alternative by eliminating alternative that do not fit the criteria and can be applied to the decision SNMPTN invitation path.

PEMILIHAN KEPLING TELADAN MENERAPKAN METODE RANK ORDER CENTROID (ROC) DAN METODE ADDITIVE RATIO ASSESSMENT (ARAS) DI KECAMATAN MEDAN AREA

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Liza Handayani ◽  
Muhammad Syahrizal ◽  
Kennedi Tampubolon
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Rank Order ◽  
Priority Level ◽  
The Village ◽  
Field Area ◽  
Selection Of

The head of the environment is an extension of the head of the village head in assisting or providing services to the community both in the administration of administration in the village and to other problems. It is natural for a kepling to be appreciated for their performance during their special tenure in the kecamatan field area. Previously, the selection of a dipling in a sub-district was very inefficient and seemed unfair for this exemplary selection to use a system to produce an accurate value, and no intentional element. To overcome the process of selecting an exemplary kepling that experiences these obstacles by using an application called a Decision Support System. Decision Support System (SPK) is a system that can solve a problem, and this system is also assisted with several methods, namely the Rank Order Centroid (ROC) method that can assign weight values to each of the criteria based on their priority level. And to do the ranking or determine an exemplary set using the Additive Ratio Assessment (ARAS) method, this method provides decision making that takes decisions based on ranking or the highest value.Keywords: Head of Medan Area Subdistrict, SPK, Centroid Rank Order, Additive Ratio Assessment (ARAS).

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