scholarly journals Optimization of Multi-Quality Water Networks: Can Simple Optimization Heuristics Compete with Nonlinear Solvers?

Water ◽  
2021 ◽  
Vol 13 (16) ◽  
pp. 2209
Author(s):  
Mashor Housh
Keyword(s):  
Large Scale ◽  
Optimal Solution ◽  
Global Optimality ◽  
Water Networks ◽  
Local Optima ◽  
Large Scale Problems ◽  
Nonlinear Solvers ◽  
Quality Aspects ◽  
Optimization Heuristics ◽  
Quality Water

Optimal management of water systems tends to be very complex, especially when water quality aspects are included. This paper addresses the management of multi-quality water networks over a fixed time horizon. The problem is formulated as an optimization program that minimizes cost by determining the optimal flow distribution that satisfies the water quantity and quality requirement in the demand nodes. The resulted model is nonlinear and non-convex due to bilinear terms in the mass balance equations of blending multi-quality flow. This results in several local optima, making the process of solving large-scale problems to global optimality very challenging. One classical approach to deal with this challenge is to use a multi-start procedure in which off-the-shelf local optimization solvers are initialized with several random initial points. Then the final optimal solution is considered as the lowest objective value over the different runs. This will lead to a cumbersome and slow solution process for large-scale problems. In light of the above, this study supports using ultra-fast simple optimization heuristics, which despite their moderate accuracy, can still reach the optimum solution when run many times using a multi-start procedure. As such, the final solution from simple optimization heuristics can compete with off-the-shelf nonlinear solvers in terms of accuracy and efficiency. The paper presents a simple optimization heuristic, which is specially tailored for the problem and compares its performance with a state-of-the-art nonlinear solver on large-scale systems.

Investigating of Hybrid Meta-Heuristics to Solve the Large-Scale Multi-Source Weber Problems and Performance Measuring of them with Statistical Tests

2013 ◽  
pp. 171-197
Author(s):  
Abdolsalam Ghaderi
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Large Scale ◽  
Statistical Tests ◽  
Hybrid Algorithms ◽  
Neighborhood Search ◽  
Swarm Optimization ◽  
Local Optima ◽  
Location Allocation ◽  
Large Scale Problems

The location–allocation problems are a class of complicated optimization problems that requires finding sites for m facilities and to simultaneously allocate n customers to those facilities to minimize the total transportation costs. Indeed, these problems, belonging to the class NP-hard, have a lot of local optima solutions. In this chapter, three hybrid meta-heuristics: genetic algorithm, variable neighborhood search and particle swarm optimization, and a hybrid local search approach. These are investigated to solve the uncapacitated continuous location-allocation problem (multi-source Weber problem). In this regard, alternate location allocation and exchange heuristics are used to find the local optima of the problem within the framework of hybrid algorithms. In addition, some large-scale problems are employed to measure the effectiveness and efficiency of hybrid algorithms. Obtained results from these heuristics are compared with local search methods and with each other. The experimental results show that the hybrid meta-heuristics produce much better solutions to solve large-scale problems. Moreover, the results of two non-parametric statistical tests detected a significant difference in hybrid algorithms such that the hybrid variable neighborhood search and particle swarm optimization algorithm outperform the others.

Simultaneous optimisation of large-scale problems of heat-integrated water networks

Energy ◽  
2021 ◽  
pp. 121354
Author(s):  
Nidret Ibrić ◽  
Elvis Ahmetović ◽  
Zdravko Kravanja ◽  
Ignacio E. Grossmann
Keyword(s):  
Large Scale ◽  
Water Networks ◽  
Large Scale Problems

A Comparison of Learning Classifier Systems’ Rule Compaction Algorithms for Knowledge Visualization

2021 ◽  
Vol 1 (3) ◽  
pp. 1-38
Author(s):  
Yi Liu ◽  
Will N. Browne ◽  
Bing Xue
Keyword(s):  
Large Scale ◽  
Optimal Solution ◽  
Population Level ◽  
Learning Classifier Systems ◽  
Classifier Systems ◽  
Rule Based ◽  
Stochastic Nature ◽  
Learning Classifier ◽  
Large Scale Problems ◽  
First Time

Learning Classifier Systems (LCSs) are a paradigm of rule-based evolutionary computation (EC). LCSs excel in data-mining tasks regarding helping humans to understand the explored problem, often through visualizing the discovered patterns linking features to classes. Due to the stochastic nature of EC, LCSs unavoidably produce and keep redundant rules, which obscure the patterns. Thus, rule compaction methods are invoked to produce a better population by removing problematic rules. Previously, compaction methods have neither been tested on large-scale problems nor been assessed on the performance of capturing patterns. We review and test the most popular compaction algorithms, finding that across multiple LCSs’ populations for the same task, although the redundant rules can be different, the accurate rules are common. Furthermore, the patterns contained consistently refer to the nature of the explored domain, e.g., the data distribution or the importance of features for determining actions. This extends the [ O ] set hypothesis proposed by Butz et al. [1], in which an LCS is expected to evolve a minimal number of non-overlapped rules to represent an addressed domain. Two new compaction algorithms are introduced to search at the rule level and the population level by compacting multiple LCSs’ populations. Two visualization methods are employed for verifying the interpretability of these populations. Successful compaction is demonstrated on complex and real problems with clean datasets, e.g., the 11-bits Majority-On problem that requires 924 different interacting rules in the optimal solution to be uniquely identified to enable correct visualization. For the first time, the patterns contained in learned models for the large-scale 70-bits Multiplexer problem are visualized successfully.

scholarly journals Multi-Country Scale Assessment of Available Energy Recovery Potential Using Micro-Hydropower in Drinking, Pressurised Irrigation and Wastewater Networks, Covering Part of the EU

Water ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 899
Author(s):  
Djordje Mitrovic ◽  
Miguel Crespo Chacón ◽  
Aida Mérida García ◽  
Jorge García Morillo ◽  
Juan Antonio Rodríguez Diaz ◽  
...  
Keyword(s):  
Drinking Water ◽  
Northern Ireland ◽  
Energy Recovery ◽  
Large Scale ◽  
Significant Proportion ◽  
Value Added ◽  
Cost Effective ◽  
Net Energy ◽  
Water Networks ◽  
Scale Assessment

Studies have shown micro-hydropower (MHP) opportunities for energy recovery and CO2 reductions in the water sector. This paper conducts a large-scale assessment of this potential using a dataset amassed across six EU countries (Ireland, Northern Ireland, Scotland, Wales, Spain, and Portugal) for the drinking water, irrigation, and wastewater sectors. Extrapolating the collected data, the total annual MHP potential was estimated between 482.3 and 821.6 GWh, depending on the assumptions, divided among Ireland (15.5–32.2 GWh), Scotland (17.8–139.7 GWh), Northern Ireland (5.9–8.2 GWh), Wales (10.2–8.1 GWh), Spain (375.3–539.9 GWh), and Portugal (57.6–93.5 GWh) and distributed across the drinking water (43–67%), irrigation (51–30%), and wastewater (6–3%) sectors. The findings demonstrated reductions in energy consumption in water networks between 1.7 and 13.0%. Forty-five percent of the energy estimated from the analysed sites was associated with just 3% of their number, having a power output capacity >15 kW. This demonstrated that a significant proportion of energy could be exploited at a small number of sites, with a valuable contribution to net energy efficiency gains and CO2 emission reductions. This also demonstrates cost-effective, value-added, multi-country benefits to policy makers, establishing the case to incentivise MHP in water networks to help achieve the desired CO2 emissions reductions targets.

scholarly journals Certifying Global Optimality for the L∞-Norm Computation of Large-Scale Descriptor Systems

2020 ◽  
Vol 53 (2) ◽  
pp. 4279-4284
Author(s):  
P. Schwerdtner ◽  
E. Mengi ◽  
M. Voigt
Keyword(s):  
Large Scale ◽  
Descriptor Systems ◽  
Global Optimality

scholarly journals Investigation of Improved Cooperative Coevolution for Large-Scale Global Optimization Problems

Algorithms ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 146
Author(s):  
Aleksei Vakhnin ◽  
Evgenii Sopov
Keyword(s):  
Global Optimization ◽  
Evolutionary Algorithms ◽  
Numerical Experiments ◽  
Large Scale ◽  
Optimization Problems ◽  
State Of The Art ◽  
Fixed Number ◽  
High Dimensional ◽  
Cooperative Coevolution ◽  
Large Scale Problems

Modern real-valued optimization problems are complex and high-dimensional, and they are known as “large-scale global optimization (LSGO)” problems. Classic evolutionary algorithms (EAs) perform poorly on this class of problems because of the curse of dimensionality. Cooperative Coevolution (CC) is a high-performed framework for performing the decomposition of large-scale problems into smaller and easier subproblems by grouping objective variables. The efficiency of CC strongly depends on the size of groups and the grouping approach. In this study, an improved CC (iCC) approach for solving LSGO problems has been proposed and investigated. iCC changes the number of variables in subcomponents dynamically during the optimization process. The SHADE algorithm is used as a subcomponent optimizer. We have investigated the performance of iCC-SHADE and CC-SHADE on fifteen problems from the LSGO CEC’13 benchmark set provided by the IEEE Congress of Evolutionary Computation. The results of numerical experiments have shown that iCC-SHADE outperforms, on average, CC-SHADE with a fixed number of subcomponents. Also, we have compared iCC-SHADE with some state-of-the-art LSGO metaheuristics. The experimental results have shown that the proposed algorithm is competitive with other efficient metaheuristics.

scholarly journals Temporal concatenation for Markov decision processes

2021 ◽  
pp. 1-28
Author(s):  
Ruiyang Song ◽  
Kuang Xu
Keyword(s):  
Markov Decision Processes ◽  
Large Scale ◽  
Optimal Solution ◽  
Upper Bounds ◽  
Black Box ◽  
Decision Processes ◽  
Optimal Solutions ◽  
Wide Range ◽  
Markov Decision ◽  
Speed Up

We propose and analyze a temporal concatenation heuristic for solving large-scale finite-horizon Markov decision processes (MDP), which divides the MDP into smaller sub-problems along the time horizon and generates an overall solution by simply concatenating the optimal solutions from these sub-problems. As a “black box” architecture, temporal concatenation works with a wide range of existing MDP algorithms. Our main results characterize the regret of temporal concatenation compared to the optimal solution. We provide upper bounds for general MDP instances, as well as a family of MDP instances in which the upper bounds are shown to be tight. Together, our results demonstrate temporal concatenation's potential of substantial speed-up at the expense of some performance degradation.

scholarly journals Fault diagnosis of industrial robot reducer by an extreme learning machine with a level-based learning swarm optimizer

2021 ◽  
Vol 13 (5) ◽  
pp. 168781402110195
Author(s):  
Jianwen Guo ◽  
Xiaoyan Li ◽  
Zhenpeng Lao ◽  
Yandong Luo ◽  
Jiapeng Wu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Extreme Learning Machine ◽  
Large Scale ◽  
Production Efficiency ◽  
Industrial Robot ◽  
Optimal Solution ◽  
Industrial Robots ◽  
Generalization Performance ◽  
Gradient Descent Algorithm ◽  
Learning Machine

Fault diagnosis is of great significance to improve the production efficiency and accuracy of industrial robots. Compared with the traditional gradient descent algorithm, the extreme learning machine (ELM) has the advantage of fast computing speed, but the input weights and the hidden node biases that are obtained at random affects the accuracy and generalization performance of ELM. However, the level-based learning swarm optimizer algorithm (LLSO) can quickly and effectively find the global optimal solution of large-scale problems, and can be used to solve the optimal combination of large-scale input weights and hidden biases in ELM. This paper proposes an extreme learning machine with a level-based learning swarm optimizer (LLSO-ELM) for fault diagnosis of industrial robot RV reducer. The model is tested by combining the attitude data of reducer gear under different fault modes. Compared with ELM, the experimental results show that this method has good stability and generalization performance.

scholarly journals Hybrid PSO-SA Type Algorithms for Multimodal Function Optimization and Reducing Energy Consumption in Embedded Systems

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Lhassane Idoumghar ◽  
Mahmoud Melkemi ◽  
René Schott ◽  
Maha Idrissi Aouad
Keyword(s):  
Energy Consumption ◽  
Embedded Systems ◽  
Evolutionary Algorithm ◽  
Optimal Solution ◽  
Function Optimization ◽  
Time Saving ◽  
Local Optima ◽  
Hybrid Evolutionary Algorithm ◽  
Multimodal Function Optimization ◽  
Multimodal Function

The paper presents a novel hybrid evolutionary algorithm that combines Particle Swarm Optimization (PSO) and Simulated Annealing (SA) algorithms. When a local optimal solution is reached with PSO, all particles gather around it, and escaping from this local optima becomes difficult. To avoid premature convergence of PSO, we present a new hybrid evolutionary algorithm, called HPSO-SA, based on the idea that PSO ensures fast convergence, while SA brings the search out of local optima because of its strong local-search ability. The proposed HPSO-SA algorithm is validated on ten standard benchmark multimodal functions for which we obtained significant improvements. The results are compared with these obtained by existing hybrid PSO-SA algorithms. In this paper, we provide also two versions of HPSO-SA (sequential and distributed) for minimizing the energy consumption in embedded systems memories. The two versions, of HPSO-SA, reduce the energy consumption in memories from 76% up to 98% as compared to Tabu Search (TS). Moreover, the distributed version of HPSO-SA provides execution time saving of about 73% up to 84% on a cluster of 4 PCs.

Sign in / Sign up

Export Citation Format

Share Document