scholarly journals Visualisation of the combinatorial effects within evolutionary algorithms: the compass plot

2020 ◽  
Author(s):  
Qi Wang ◽  
Miaoting Guan ◽  
Wen Huang ◽  
Libing Wang ◽  
Zhihong Wang ◽  
...  
Keyword(s):  
Evolutionary Algorithms ◽  
Real World ◽  
Distribution System ◽  
Water Distribution ◽  
Cost Effective ◽  
Considerable Change ◽  
Nsga Ii ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Real World Problems

Abstract Applications of evolutionary algorithms (EAs) to real-world problems are usually hindered due to parameterisation issues and computational efficiency. This paper shows how the combinatorial effects related to the parameterisation issues of EAs can be visualised and extracted by the so-called compass plot. This new plot is inspired by the traditional Chinese compass used for navigation and geomantic detection. We demonstrate the value of the proposed compass plot in two scenarios with application to the optimal design of the Hanoi water distribution system. One is to identify the dominant parameters in the well-known NSGA-II. The other is to seek the efficient combinations of search operators embedded in Borg, which uses an ensemble of search operators by auto-adapting their use at runtime to fit an optimisation problem. As such, the implicit and vital interdependency among parameters and search operators can be intuitively demonstrated and identified. In particular, the compass plot revealed some counter-intuitive relationships among the algorithm parameters that led to a considerable change in performance. The information extracted, in turn, facilitates a deeper understanding of EAs and better practices for real-world cases, which eventually leads to more cost-effective decision-making.

scholarly journals Leakage Reduction in Water Distribution Systems with Efficient Placement and Control of Pressure Reducing Valves Using Soft Computing Techniques

2017 ◽  
Vol 7 (2) ◽  
pp. 1528-1534 ◽  
Author(s):  
A. Gupta ◽  
N. Bokde ◽  
D. Marathe ◽  
K. Kulat
Keyword(s):  
Distribution System ◽  
Distribution Systems ◽  
Water Distribution ◽  
Reduction Rate ◽  
Cost Effective ◽  
Leakage Rate ◽  
Nsga Ii ◽  
Leakage Reduction ◽  
Soft Computing Techniques ◽  
And Control

Reduction of leakages in a water distribution system (WDS) is one of the major concerns of water industries. Leakages depend on pressure, hence installing pressure reducing valves (PRVs) in the water network is a successful techniques for reducing leakages. Determining the number of valves, their locations, and optimal control setting are the challenges faced. This paper presents a new algorithm-based rule for determining the location of valves in a WDS having a variable demand pattern, which results in more favorable optimization of PRV localization than that caused by previous techniques. A multiobjective genetic algorithm (NSGA-II) was used to determine the optimized control value of PRVs and to minimize the leakage rate in the WDS. Minimum required pressure was maintained at all nodes to avoid pressure deficiency at any node. Proposed methodology is applied in a benchmark WDS and after using PRVs, the average leakage rate was reduced by 6.05 l/s (20.64%), which is more favorable than the rate obtained with the existing techniques used for leakage control in the WDS. Compared with earlier studies, a lower number of PRVs was required for optimization, thus the proposed algorithm tends to provide a more cost-effective solution. In conclusion, the proposed algorithm leads to more favorable optimized localization and control of PRV with improved leakage reduction rate.

scholarly journals Comparison of Searching Behaviour of Three Evolutionary Algorithms Applied to Water Distribution System Design Optimization

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 695 ◽  
Author(s):  
Weiwei Bi ◽  
Yihui Xu ◽  
Hongyu Wang
Keyword(s):  
Evolutionary Algorithms ◽  
Real Time ◽  
Distribution System ◽  
Optimization Design ◽  
Distribution Systems ◽  
Water Distribution ◽  
Optimal Solutions ◽  
Great Ability ◽  
Searching Behaviour ◽  
Run Time

Over the past few decades, various evolutionary algorithms (EAs) have been applied to the optimization design of water distribution systems (WDSs). An important research area is to compare the performance of these EAs, thereby offering guidance for the selection of the appropriate EAs for practical implementations. Such comparisons are mainly based on the final solution statistics and, hence, are unable to provide knowledge on how different EAs reach the final optimal solutions and why different EAs performed differently in identifying optimal solutions. To this end, this paper aims to compare the real-time searching behaviour of three widely used EAs, which are genetic algorithms (GAs), the differential evolution (DE) algorithm and the ant colony optimization (ACO). These three EAs are applied to five WDS benchmarking case studies with different scales and complexities, and a set of five metrics are used to measure their run-time searching quality and convergence properties. Results show that the run-time metrics can effectively reveal the underlying searching mechanisms associated with each EA, which significantly goes beyond the knowledge from the traditional end-of-run solution statistics. It is observed that the DE is able to identify better solutions if moderate and large computational budgets are allowed due to its great ability in maintaining the balance between the exploration and exploitation. However, if the computational resources are rather limited or the decision has to be made in a very short time (e.g., real-time WDS operation), the GA can be a good choice as it can always identify better solutions than the DE and ACO at the early searching stages. Based on the results, the ACO performs the worst for the five case study considered. The outcome of this study is the offer of guidance for the algorithm selection based on the available computation resources, as well as knowledge into the EA’s underlying searching behaviours.

scholarly journals A delayed differentiation multiproduct model with the outsourcing of common parts, overtime strategy for end products, and quality reassurance

2021 ◽  
pp. 143-158 ◽  
Author(s):  
Singa Wang Chiu ◽  
Victoria Chiu ◽  
Ming-Hon Hwang ◽  
Yuan-Shyi Peter Chiu
Keyword(s):  
Cost Effective ◽  
Sensitivity Analyses ◽  
Optimization Approach ◽  
Limited Capacity ◽  
Numerical Illustration ◽  
Delayed Differentiation ◽  
Modeling Analysis ◽  
End Products ◽  
Effective Decision ◽  
Effective Decision Making

Production planners today must simultaneously face with the time and quality demands of various goods externally and meet limited capacity internally. This study presents a two-stage delayed- differentiation multiproduct model that considers the outsourcing options for common parts, overtime strategy for end products, and quality reassurance to assist in making fabrication runtime decisions that are cost-effective. Stage one produces all necessary common intermediate components for end products. To reduce stage one’s utilization/uptime, this study adopts a partial outsourcing option. Stage two uses an overtime strategy to fabricate end products that further shorten the uptime. The production processes in both phases are assumed to be imperfect. This study employs the reworking/scrapping of random faulty items to reassure product quality. The researchers build a model to depict the proposed problem’s characteristics and used the mathematical modeling, analysis, and optimization approach to determine the best rotation cycle length that minimizes the system’s expenses. Further, in this study, the researchers provide sensitivity analyses and a numerical illustration, which validate the result’s applicability and exhibit its capability. This result contributes to practical multiproduct-fabrication by (1) deriving the optimal manufacturing policy for a delayed-differentiation multiproduct system with dual uptime reduction policies and quality reassurance; and (2) offering a decisional model that allows production planners to explore the collective/separate effect of a quality-ensured and dual uptime reduction strategy on a problem’s operating policy and crucial system performance indicators, which assists in cost-effective decision-making.

Affective Goal and Task Selection for Social Robots

2009 ◽  
pp. 74-87 ◽  
Author(s):  
Matthias Scheutz ◽  
Paul Schermerhorn
Keyword(s):  
Decision Making ◽  
Real World ◽  
Social Robots ◽  
Human Robot Interaction ◽  
Robot Interaction ◽  
Task Selection ◽  
Resource Limitations ◽  
Effective Decision ◽  
Effective Decision Making ◽  
Selection Of

Effective decision-making under real-world conditions can be very difficult as purely rational methods of decision-making are often not feasible or applicable. Psychologists have long hypothesized that humans are able to cope with time and resource limitations by employing affective evaluations rather than rational ones. In this chapter, we present the distributed integrated affect cognition and reflection architecture DIARC for social robots intended for natural human-robot interaction and demonstrate the utility of its human-inspired affect mechanisms for the selection of tasks and goals. Specifically, we show that DIARC incorporates affect mechanisms throughout the architecture, which are based on “evaluation signals” generated in each architectural component to obtain quick and efficient estimates of the state of the component, and illustrate the operation and utility of these mechanisms with examples from human-robot interaction experiments.

Deductive Sort and Climbing Sort: New Methods for Non-Dominated Sorting

2012 ◽  
Vol 20 (1) ◽  
pp. 1-26 ◽  
Author(s):  
Kent McClymont ◽  
Ed Keedwell
Keyword(s):  
Computational Complexity ◽  
Evolutionary Algorithms ◽  
Real World ◽  
Future Generations ◽  
Nsga Ii ◽  
New Methods ◽  
Research Fields ◽  
Dominance Relationships ◽  
Large Populations ◽  
Novel Methods

In recent years an increasing number of real-world many-dimensional optimisation problems have been identified across the spectrum of research fields. Many popular evolutionary algorithms use non-dominance as a measure for selecting solutions for future generations. The process of sorting populations into non-dominated fronts is usually the controlling order of computational complexity and can be expensive for large populations or for a high number of objectives. This paper presents two novel methods for non-dominated sorting: deductive sort and climbing sort. The two new methods are compared to the fast non-dominated sort of NSGA-II and the non-dominated rank sort of the omni-optimizer. The results demonstrate the improved efficiencies of the deductive sort and the reductions in comparisons that can be made when applying inferred dominance relationships defined in this paper.

Sign in / Sign up

Export Citation Format

Share Document