scholarly journals Bio-Inspired Optimization of Sustainable Energy Systems: A Review

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Yu-Jun Zheng ◽  
Sheng-Yong Chen ◽  
Yao Lin ◽  
Wan-Liang Wang
Keyword(s):  
Optimization Problems ◽  
Sustainable Energy ◽  
Biological Evolution ◽  
Optimization Methods ◽  
Energy Development ◽  
Future Research ◽  
Planning And Control ◽  
Conventional Optimization ◽  
Development Literature ◽  
And Control

Sustainable energy development always involves complex optimization problems of design, planning, and control, which are often computationally difficult for conventional optimization methods. Fortunately, the continuous advances in artificial intelligence have resulted in an increasing number of heuristic optimization methods for effectively handling those complicated problems. Particularly, algorithms that are inspired by the principles of natural biological evolution and/or collective behavior of social colonies have shown a promising performance and are becoming more and more popular nowadays. In this paper we summarize the recent advances in bio-inspired optimization methods, including artificial neural networks, evolutionary algorithms, swarm intelligence, and their hybridizations, which are applied to the field of sustainable energy development. Literature reviewed in this paper shows the current state of the art and discusses the potential future research trends.

scholarly journals An Adaptive Particle Swarm Optimization Algorithm for Unconstrained Optimization

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Feng Qian ◽  
Mohammad Reza Mahmoudi ◽  
Hamïd Parvïn ◽  
Kim-Hung Pho ◽  
Bui Anh Tuan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Methods ◽  
Global Optimum ◽  
Initial Population ◽  
Swarm Optimization ◽  
Conventional Optimization

Conventional optimization methods are not efficient enough to solve many of the naturally complicated optimization problems. Thus, inspired by nature, metaheuristic algorithms can be utilized as a new kind of problem solvers in solution to these types of optimization problems. In this paper, an optimization algorithm is proposed which is capable of finding the expected quality of different locations and also tuning its exploration-exploitation dilemma to the location of an individual. A novel particle swarm optimization algorithm is presented which implements the conditioning learning behavior so that the particles are led to perform a natural conditioning behavior on an unconditioned motive. In the problem space, particles are classified into several categories so that if a particle lies within a low diversity category, it would have a tendency to move towards its best personal experience. But, if the particle’s category is with high diversity, it would have the tendency to move towards the global optimum of that category. The idea of the birds’ sensitivity to its flying space is also utilized to increase the particles’ speed in undesired spaces in order to leave those spaces as soon as possible. However, in desirable spaces, the particles’ velocity is reduced to provide a situation in which the particles have more time to explore their environment. In the proposed algorithm, the birds’ instinctive behavior is implemented to construct an initial population randomly or chaotically. Experiments provided to compare the proposed algorithm with the state-of-the-art methods show that our optimization algorithm is one of the most efficient and appropriate ones to solve the static optimization problems.

Genetic Algorithms for Optimization of Building Envelopes and the Design and Control of HVAC Systems

2003 ◽  
Vol 125 (3) ◽  
pp. 343-351 ◽  
Author(s):  
L. G. Caldas ◽  
L. K. Norford
Keyword(s):  
Genetic Algorithms ◽  
Optimization Problems ◽  
Optimization Methods ◽  
Optimization Method ◽  
Hvac Systems ◽  
Design Problems ◽  
Building Walls ◽  
Building Form ◽  
And Control ◽  
Future Work

Many design problems related to buildings involve minimizing capital and operating costs while providing acceptable service. Genetic algorithms (GAs) are an optimization method that has been applied to these problems. GAs are easily configured, an advantage that often compensates for a sacrifice in performance relative to optimization methods selected specifically for a given problem, and have been shown to give solutions where other methods cannot. This paper reviews the basics of GAs, emphasizing multi-objective optimization problems. It then presents several applications, including determining the size and placement of windows and the composition of building walls, the generation of building form, and the design and operation of HVAC systems. Future work is identified, notably interfaces between a GA and both simulation and CAD programs.

scholarly journals The challenges of project management in small and medium-sized enterprises: a literature review based on bibliometric software and content analysis

2020 ◽  
Vol 27 (1) ◽  
Author(s):  
Letícia de Almeida Parizotto ◽  
Aldo Tonso ◽  
Marly Monteiro de Carvalho
Keyword(s):  
Content Analysis ◽  
Project Management ◽  
Management Practices ◽  
Future Research ◽  
High Turnover ◽  
Planning And Control ◽  
Literature Overview ◽  
Systems Collaboration ◽  
Research Gap ◽  
And Control

Abstract: The purpose of this paper is to design an overview about Project Management (PM) in Small and Medium-Sized Enterprises (SMEs) by analysing the evolution of publications and the main topics since 1996 to 2016 to motivate future research that helps SMEs to apply PM practices more efficiently. This study performed bibliometrics associated with content analysis of publications collected in scientific bases Web of Science and Scopus and in the periodic International Journal of Project Management. For that, the software VOSviewer, Nvivo, Minitab, and Excel were used in the analyses. The scan of 235 papers about Project Management in SMEs supported a literature overview. Furthermore, four thematic categories are highlighted: Project Management Practices, Planning and Control Systems, Collaboration, and Knowledge Management. Moreover, it was observed that SMEs requires a lighter PM methodology, focused on people and flexible. Besides that, the results show that the main challenges involve a lack of resources and qualified people and the high turnover. However, overcoming these issues, PM can benefit growth and innovation in SMEs. Therefore, this study presents a conceptual framework of benefits and challenges in Project Management in SMEs, reducing the research gap. Furthermore, recommendations for future research, mainly in Brazil, are given.

scholarly journals COVID-19 and Sustainable Energy Development: Agendas for Future Research

2020 ◽  
Vol 4 ◽  
pp. 20-25
Author(s):  
Kevin Lo
Keyword(s):  
Sustainable Energy ◽  
Energy Development ◽  
Future Research ◽  
Long Term Effects ◽  
The Sustainable Development ◽  
Short And Long Term ◽  
Development Agendas ◽  
The Impact ◽  
Key Questions

The COVID-19 pandemic is having a massive impact on and may fundamentally change the pathways and trajectories of sustainable energy development. This article examines the impact of COVID-19 on Asia’s sustainable energy development and proposes agendas for future energy research in response to the pandemic. The review and research agendas are oriented towards achieving the Sustainable Development Goal 7 (SDG 7), ensuring access to affordable, reliable, sustainable and modern energy for all. The following three key questions need to be addressed by researchers: (1) In what ways does COVID-19 make sustainable energy development more important than ever? (2) What are the short- and long-term effects of COVID-19 on sustainable energy development? (3) How can responses to COVID-19 meet the objectives of sustainable energy development?

scholarly journals Bio-Inspired Approaches for Smart Energy Management: State of the Art and Challenges

2020 ◽  
Vol 12 (20) ◽  
pp. 8495
Author(s):  
Tri-Hai Nguyen ◽  
Luong Vuong Nguyen ◽  
Jason J. Jung ◽  
Israel Edem Agbehadji ◽  
Samuel Ofori Frimpong ◽  
...  
Keyword(s):  
Energy Management ◽  
Smart Grids ◽  
Optimization Problems ◽  
State Of The Art ◽  
Biological Evolution ◽  
Optimization Methods ◽  
Management Systems ◽  
Collective Behaviors ◽  
Energy Management Systems ◽  
Smart Energy Management

Sustainable energy development consists of design, planning, and control optimization problems that are typically complex and computationally challenging for traditional optimization approaches. However, with developments in artificial intelligence, bio-inspired algorithms mimicking the concepts of biological evolution in nature and collective behaviors in societies of agents have recently become popular and shown potential success for these issues. Therefore, we investigate the latest research on bio-inspired approaches for smart energy management systems in smart homes, smart buildings, and smart grids in this paper. In particular, we give an overview of the well-known and emerging bio-inspired algorithms, including evolutionary-based and swarm-based optimization methods. Then, state-of-the-art studies using bio-inspired techniques for smart energy management systems are presented. Lastly, open challenges and future directions are also addressed to improve research in this field.

Motion Control in Magnetic Microrobotics: From Individual and Multiple Robots to Swarms

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Lidong Yang ◽  
Li Zhang
Keyword(s):  
Motion Control ◽  
Autonomous Systems ◽  
System Structure ◽  
Future Research ◽  
Annual Review ◽  
Publication Date ◽  
Current Feedback ◽  
Loop Control ◽  
Planning And Control ◽  
And Control

Magnetic microrobotics has undergone approximately 20 years of development, and the robotics and control communities have contributed significant theoretical and practical results to the motion control aspects of this field. This article introduces fundamental motion principles covering individual, multiagent, and swarm control and critically reviews the state of the art along with representative results. It then describes closed-loop control (an important part of this field), including the system structure, current motion planning and control methods, and current feedback approaches. As the development of motion control in magnetic microrobotics is far from complete, especially for swarm control, its current limitations are discussed. Finally, we conclude with several challenges and future research directions. Expected final online publication date for the Annual Review of Control, Robotics, and Autonomous Systems, Volume 4 is May 3, 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

scholarly journals A Fast Integrated Planning and Control Framework for Autonomous Driving via Imitation Learning

2018 ◽  
Author(s):  
Liting Sun ◽  
Cheng Peng ◽  
Wei Zhan ◽  
Masayoshi Tomizuka
Keyword(s):  
Nonlinear Models ◽  
Autonomous Driving ◽  
Optimization Methods ◽  
Imitation Learning ◽  
Short Term ◽  
Model Based ◽  
Planning And Control ◽  
And Control ◽  
Optimal Driving

Safety and efficiency are two key elements for planning and control in autonomous driving. Theoretically, model-based optimization methods, such as Model Predictive Control (MPC), can provide such optimal driving policies. Their computational complexity, however, grows exponentially with horizon length and number of surrounding vehicles. This makes them impractical for real-time implementation, particularly when nonlinear models are considered. To enable a fast and approximately optimal driving policy, we propose a safe imitation framework, which contains two hierarchical layers. The first layer, defined as the policy layer, is represented by a neural network that imitates a long-term expert driving policy via imitation learning. The second layer, called the execution layer, is a short-term model-based optimal controller that tracks and further fine-tunes the reference trajectories proposed by the policy layer with guaranteed short-term collision avoidance. Moreover, to reduce the distribution mismatch between the training set and the real world, Dataset Aggregation is utilized so that the performance of the policy layer can be improved from iteration to iteration. Several highway driving scenarios are demonstrated in simulations, and the results show that the proposed framework can achieve similar performance as sophisticated long-term optimization approaches but with significantly improved computational efficiency.

Parallelization of Evolution Strategy on a Workstation-Cluster and its Application to Discrete Structural Optimization

1994 ◽  
Author(s):  
Jianbo Cai ◽  
Georg Thierauf
Keyword(s):  
Parallel Computing ◽  
Structural Optimization ◽  
Discrete Optimization ◽  
Optimization Problems ◽  
Biological Evolution ◽  
Computing Environment ◽  
Design Point ◽  
Workstation Cluster ◽  
Conventional Optimization ◽  
Discrete Optimization Problems

Abstract Evolution strategies (ESs) imitate biological evolution and have two characteristics that differ from other conventional optimization algorithms: (a) ESs use randomized operators instead of the usual deterministic ones; (b) instead of a single design point, the ESs work simultaneously with a population of design points in the space of variables. The second characteristic allows for an implementation in a parallel computing environment. In this paper the application of ESs for the solution of discrete optimization problems and its parallelization are described.

Hybrid Optimization Techniques for Industrial Production Planning

2014 ◽  
pp. 41-68 ◽  
Author(s):  
P. Vasant
Keyword(s):  
Global Optimization ◽  
Production Planning ◽  
Industrial Production ◽  
Optimization Problems ◽  
Hybrid Methods ◽  
Optimization Methods ◽  
Optimization Techniques ◽  
Search Pattern ◽  
Future Research ◽  
Hybrid Optimization Techniques

This chapter provides a review of new hybrid methods that deal with the continuous local and global optimization problems for constrained industrial production planning problems. In this chapter, details about all types of optimization methods and approaches for the local and global optimization are highlighted. Altogether there are eight famous methods in hybrid evolutionary optimization. In this research, the hybridization between evolutionary algorithms and other heuristic approaches such as simulated annealing, line search, pattern search, and mesh adaptive direct search are adopted. A particular evolutionary computation approach of genetic algorithm is used in this hybridization process. An intelligent performance analysis table is suggested in this chapter which is significantly important for decision makers and implementers in the industrial engineering of production planning. A brief summary on the conclusions of the main contributions and achievements in this chapter as well as future research directions are highlighted.

scholarly journals Maintenance spare parts planning and control: a framework for control and agenda for future research

2014 ◽  
pp. 1-20 ◽  
Author(s):  
Maarten Driessen ◽  
Joachim Arts ◽  
Geert-Jan van Houtum ◽  
Jan Willem Rustenburg ◽  
Bob Huisman
Keyword(s):  
Spare Parts ◽  
Future Research ◽  
Planning And Control ◽  
And Control
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