scholarly journals Drawing Inspiration From Human Design Teams For Better Search And Optimization: The Heterogeneous Simulated Annealing Teams Algorithm

2018 ◽  
Author(s):  
Christopher McComb ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Simulated Annealing ◽  
Computational Model ◽  
Engineering Design ◽  
Simulated Annealing Algorithm ◽  
Search Strategies ◽  
Design Teams ◽  
Team Members ◽  
Structure Interaction ◽  
Multi Agent ◽  
Annealing Algorithm

Insights uncovered by research in design cognition are often utilized to develop methods used by human designers; in this work such insights are used to inform and improve computational methodologies. This paper introduces the Heterogeneous Simulated Annealing Team (HSAT) algorithm, a multi-agent simulated annealing algorithm. HSAT is based on a validated computational model of human-based engineering design, and retains characteristics of the model that structure interaction between team members and allow for heterogeneous search strategies to be employed within a team. The performance of this new algorithm is compared to several other simulated annealing based algorithms on three carefully selected benchmarking functions. The HSAT algorithm provides terminal solutions that are better on average than other algorithms explored in this work.

scholarly journals Drawing Inspiration From Human Design Teams for Better Search and Optimization: The Heterogeneous Simulated Annealing Teams Algorithm

2016 ◽  
Vol 138 (4) ◽  
Author(s):  
Christopher McComb ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Simulated Annealing ◽  
Computational Model ◽  
Engineering Design ◽  
Search Strategies ◽  
Design Teams ◽  
Design Cognition ◽  
Team Members ◽  
Structure Interaction

Insights uncovered by research in design cognition are often utilized to develop methods used by human designers; in this work, such insights are used to inform and improve computational methodologies. This paper introduces the heterogeneous simulated annealing team (HSAT) algorithm, a multiagent simulated annealing (MSA) algorithm. HSAT is based on a validated computational model of human-based engineering design and retains characteristics of the model that structure interaction between team members and allow for heterogeneous search strategies to be employed within a team. The performance of this new algorithm is compared to several other simulated annealing (SA) based algorithms on three carefully selected benchmarking functions. The HSAT algorithm provides terminal solutions that are better on average than other algorithms explored in this work.

scholarly journals Heterogeneous Simulated Annealing Teams: An Optimizing Search Algorithm Inspired by Engineering Design Teams

2018 ◽  
Author(s):  
Christopher McComb ◽  
Jonathan Cagan ◽  
Kenneth Kotovsky
Keyword(s):  
Simulated Annealing ◽  
Engineering Design ◽  
Simulated Annealing Algorithm ◽  
Search Algorithm ◽  
Random Search ◽  
Design Teams ◽  
Design Cognition ◽  
Gradient Based ◽  
Multi Agent ◽  
Annealing Algorithms

Although insights uncovered by design cognition are often utilized to develop the methods used by human designers, using such insights to inform computational methodologies also has the potential to improve the performance of design algorithms. This paper uses insights from research on design cognition and design teams to inform a better simulated annealing search algorithm. Simulated annealing has already been established as a model of individual problem solving. This paper introduces the Heterogeneous Simulated Annealing Team (HSAT) algorithm, a multi-agent simulated annealing algorithm. Each agent controls an adaptive annealing schedule, allowing the team develop heterogeneous search strategies. Such diversity is a natural part of engineering design, and boosts performance in other multi-agent algorithms. Further, interaction between agents in HSAT is structured to mimic interaction between members of a design team. Performance is compared to several other simulated annealing algorithms, a random search algorithm, and a gradient-based algorithm. Compared to other algorithms, the team-based HSAT algorithm returns better average results with lower variance.

Hybrid Method for Solving Flexible Open Shop Scheduling Problem with Simulated Annealing Algorithm and Multi-Agent Approach

2011 ◽  
Vol 383-390 ◽  
pp. 4612-4619 ◽  
Author(s):  
Tadeusz Witkowski ◽  
Paweł Antczak ◽  
Arkadiusz Antczak
Keyword(s):  
Simulated Annealing ◽  
Simulated Annealing Algorithm ◽  
The Other ◽  
Scheduling Problem ◽  
Open Shop Scheduling ◽  
Hand Search ◽  
Shop Scheduling ◽  
Multi Agent ◽  
Annealing Algorithm ◽  
Metaheuristic Optimization Algorithm

In this study we propose metaheuristic optimization algorithm, in which simulated annealing, multi agent approach with fuzzy logic are used. On the first level of solution search the multi agent approach is used, and on the second level – the simulated annealing. Two types of routing were considered: a serial and a parallel one. The multi-agent approach emphasizes flexibility rather than the optimality of solutions. On the other hand, search approaches such as simulated annealing, which focus more on the optimality of solutions.

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