Relevant bibliographies by topics / Bio-inspired optimisation method

Academic literature on the topic 'Bio-inspired optimisation method'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Bio-inspired optimisation method"

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Sabrina, Titri, Azli Hadjer, Izeboudjen Nouma, and Larbes Cherif. "A bio inspired maximum power point tracking controller for PV systems under partial shading conditions." Indonesian Journal of Electrical Engineering and Computer Science 28, no. 3 (October 7, 2022): 1425. http://dx.doi.org/10.11591/ijeecs.v28.i3.pp1425-1436.

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Maximum power point tracking (MPPT) is a technique used in extracting the maximum power from a photovoltaic panel (PV) under different weather conditions. The last decade has witnessed a wide variety of algorithm based on MPPT controllers, ranging from simple to more complexe ones. Each of them has its own advantage and disadvantage. Hence, it is crutial to propose methods that are both simple and effective to track and maintain the MPPT of a PV system, even under partial shading conditions. In this study, we propose a new bio inspired method namely seagull optimization algorithm (SOA) for solving the MPPT problem in a PV system. To evaluate the proposed SOA _MPPT performance in terms of accuracy, convergence and stability, a simulation methodology is used. First, by tunning the appropriate parameters, then, we consider the following scenarios: rapid change of solar irradiation, temperature, and three patterns to test partial shading effect. The results are compared with latest bio-inspired methods, namely, particle swarm optimization (PSO), Bat optimisation algorithm (BAT) and fire fly algorithm (FA). The obtained results confirm the effectiveness and robustness of the proposed controller compared to existing conventional and bio inspired controllers.
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Fard, Abdollah Kavousi, Mohammad Reza Akbari Zadeh, Bahram Dehghan, and Farzaneh Kavousi Fard. "A novel sufficient bio-inspired optimisation method based on modified krill herd algorithm to solve the economic load dispatch." International Journal of Bio-Inspired Computation 6, no. 6 (2014): 416. http://dx.doi.org/10.1504/ijbic.2014.066973.

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Kotiyal, Vaibhav, Abhilash Singh, Sandeep Sharma, Jaiprakash Nagar, and Cheng-Chi Lee. "ECS-NL: An Enhanced Cuckoo Search Algorithm for Node Localisation in Wireless Sensor Networks." Sensors 21, no. 11 (May 21, 2021): 3576. http://dx.doi.org/10.3390/s21113576.

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Node localisation plays a critical role in setting up Wireless Sensor Networks (WSNs). A sensor in WSNs senses, processes and transmits the sensed information simultaneously. Along with the sensed information, it is crucial to have the positional information associated with the information source. A promising method to localise these randomly deployed sensors is to use bio-inspired meta-heuristic algorithms. In this way, a node localisation problem is converted to an optimisation problem. Afterwards, the optimisation problem is solved for an optimal solution by minimising the errors. Various bio-inspired algorithms, including the conventional Cuckoo Search (CS) and modified CS algorithm, have already been explored. However, these algorithms demand a predetermined number of iterations to reach the optimal solution, even when not required. In this way, they unnecessarily exploit the limited resources of the sensors resulting in a slow search process. This paper proposes an Enhanced Cuckoo Search (ECS) algorithm to minimise the Average Localisation Error (ALE) and the time taken to localise an unknown node. In this algorithm, we have implemented an Early Stopping (ES) mechanism, which improves the search process significantly by exiting the search loop whenever the optimal solution is reached. Further, we have evaluated the ECS algorithm and compared it with the modified CS algorithm. While doing so, note that the proposed algorithm localised all the localisable nodes in the network with an ALE of 0.5–0.8 m. In addition, the proposed algorithm also shows an 80% decrease in the average time taken to localise all the localisable nodes. Consequently, the performance of the proposed ECS algorithm makes it desirable to implement in practical scenarios for node localisation.
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Neshat, Mehdi, Nataliia Sergiienko, Seyedali Mirjalili, Meysam Majidi Nezhad, Giuseppe Piras, and Davide Astiaso Garcia. "Multi-Mode Wave Energy Converter Design Optimisation Using an Improved Moth Flame Optimisation Algorithm." Energies 14, no. 13 (June 22, 2021): 3737. http://dx.doi.org/10.3390/en14133737.

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Ocean renewable wave power is one of the more encouraging inexhaustible energy sources, with the potential to be exploited for nearly 337 GW worldwide. However, compared with other sources of renewables, wave energy technologies have not been fully developed, and the produced energy price is not as competitive as that of wind or solar renewable technologies. In order to commercialise ocean wave technologies, a wide range of optimisation methodologies have been proposed in the last decade. However, evaluations and comparisons of the performance of state-of-the-art bio-inspired optimisation algorithms have not been contemplated for wave energy converters’ optimisation. In this work, we conduct a comprehensive investigation, evaluation and comparison of the optimisation of the geometry, tether angles and power take-off (PTO) settings of a wave energy converter (WEC) using bio-inspired swarm-evolutionary optimisation algorithms based on a sample wave regime at a site in the Mediterranean Sea, in the west of Sicily, Italy. An improved version of a recent optimisation algorithm, called the Moth–Flame Optimiser (MFO), is also proposed for this application area. The results demonstrated that the proposed MFO can outperform other optimisation methods in maximising the total power harnessed from a WEC.
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Etaati, Bahareh, Amin Abdollahi Dehkordi, Ali Sadollah, Mohammed El-Abd, and Mehdi Neshat. "A Comparative State-of-the-Art Constrained Metaheuristics Framework for TRUSS Optimisation on Shape and Sizing." Mathematical Problems in Engineering 2022 (March 26, 2022): 1–13. http://dx.doi.org/10.1155/2022/6078986.

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In order to develop the dynamic effectiveness of the structures such as trusses, the application of optimisation methods plays a significant role in improving the shape and size of elements. However, conjoining two heterogeneous variables, nodal coordinates and cross-sectional elements, makes a challenging optimisation problem that is nonlinear, multimodal, large-scale with dynamic constraints. To handle these challenges, evolutionary and swarm optimisation algorithms can be robust and practical tools and show great potential to solve such complex problems. This paper proposed a comparative truss optimisation framework to solve two large-scale structures, including 314-bar and 260-bar trusses. The proposed framework consists of twelve state-of-the-art bio-inspired algorithms. The experimental results show that the marine predators algorithm (MPA) performed best compared with other algorithms in terms of convergence speed and the quality of the proposed designs of the trusses.
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Oliveira, Paulo Moura, EJ Solteiro Pires, José Boaventura-Cunha, and Tatiana Martins Pinho. "Review of nature and biologically inspired metaheuristics for greenhouse environment control." Transactions of the Institute of Measurement and Control 42, no. 12 (March 24, 2020): 2338–58. http://dx.doi.org/10.1177/0142331220909010.

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A significant number of search and optimisation techniques whose principles seek inspiration from nature and biology phenomena have been proposed in the last decades. These methods have been successfully applied to solve a wide range of engineering problems. This is also the case of greenhouse environment control, which has been incorporating this type of techniques into its design. This paper addresses evolutionary and bio-inspired methods in the context of greenhouse environment control. Algorithm principles for reference techniques are reviewed, namely: simulated annealing, genetic algorithm, differential evolution and particle swarm optimisation. The last three techniques are considered using single and multiple objective formulations. A review of these algorithms within greenhouse environment control applications is presented, considering single and multiple objective problems, as well as their current trends.
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Benkhelifa, Elhadj, Ashutosh Tiwari, and Mohamed Abdel-Maguid. "Advanced Design Optimisation by Means of Multiobjective Evolutionary Algorithms: The Case of Two Real World Applications." Key Engineering Materials 572 (September 2013): 589–92. http://dx.doi.org/10.4028/www.scientific.net/kem.572.589.

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The Design Optimisation (DO) of Complex Systems is often a multidisciplinary task and involves multiple conflicting objectives and design constraints, where conventional methods cannot solve efficiently. This paper presents Advanced DO by Means of Evolutional Algorithms in two Real World Applications Electronics and Micro-Electro-Mechanical-Systems (MEMS). The former is presented in the context of multi-objective evolutionary synthesis and optimisation of analogue systems. As for the latter, DO of MEMS bio-mimetically is a very novel area of research, Which addresses the compelling change in the traditional landscape of the associated research disciplines by seeking to provide a novel biologically inspired computational platform for DO of micro-scale designs. This paper presents the latest advancements in the application of EAs in the DO of MEMS and analogue electronic systems and the emergence of the new area of ‘Multidisciplinary Optimisation'.
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Fan, Xue Mei, Shu Jun Zhang, Kevin Hapeshi, and Yin Sheng Yang. "Biological System Behaviours and Natural-Inspired Methods and their Applications to Supply Chain Management." Applied Mechanics and Materials 461 (November 2013): 942–58. http://dx.doi.org/10.4028/www.scientific.net/amm.461.942.

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People have learnt from biological system behaviours and structures to design and develop a number of different kinds of optimisation algorithms that have been widely used in both theoretical study and practical applications in engineering and business management. An efficient supply chain is very important for companies to survive in global competitive market. An effective SCM (supply chain management) is the key for implement an efficient supply chain. Though there have been considerable amount of study of SCM, there have been very limited publications of applying the findings from the biological system study into SCM. In this paper, through systematic literature review, various SCM issues and requirements are discussed and some typical biological system behaviours and natural-inspired algorithms are evaluated for the purpose of SCM. Then the principle and possibility are presented on how to learn the biological systems' behaviours and natural-inspired algorithms for SCM and a framework is proposed as a guide line for users to apply the knowledge learnt from the biological systems for SCM. In the framework, a number of the procedures have been presented for using XML to represent both SCM requirement and bio-inspiration data. To demonstrate the proposed framework, a case study has been presented for users to find the bio-inspirations for some particular SCM problems in automotive industry.
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Brodzicki, Andrzej, Michał Piekarski, and Joanna Jaworek-Korjakowska. "The Whale Optimization Algorithm Approach for Deep Neural Networks." Sensors 21, no. 23 (November 30, 2021): 8003. http://dx.doi.org/10.3390/s21238003.

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One of the biggest challenge in the field of deep learning is the parameter selection and optimization process. In recent years different algorithms have been proposed including bio-inspired solutions to solve this problem, however, there are many challenges including local minima, saddle points, and vanishing gradients. In this paper, we introduce the Whale Optimisation Algorithm (WOA) based on the swarm foraging behavior of humpback whales to optimise neural network hyperparameters. We wish to stress that to the best of our knowledge this is the first attempt that uses Whale Optimisation Algorithm for the optimisation task of hyperparameters. After a detailed description of the WOA algorithm we formulate and explain the application in deep learning, present the implementation, and compare the proposed algorithm with other well-known algorithms including widely used Grid and Random Search methods. Additionally, we have implemented a third dimension feature analysis to the original WOA algorithm to utilize 3D search space (3D-WOA). Simulations show that the proposed algorithm can be successfully used for hyperparameters optimization, achieving accuracy of 89.85% and 80.60% for Fashion MNIST and Reuters datasets, respectively.
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Gheraibia, Youcef, Abdelouahab Moussaoui, Youcef Djenouri, Sohag Kabir, Peng-Yeng Yin, and Smaine Mazouzi. "Penguin Search Optimisation Algorithm for Finding Optimal Spaced Seeds." International Journal of Software Science and Computational Intelligence 7, no. 2 (April 2015): 85–99. http://dx.doi.org/10.4018/ijssci.2015040105.

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This paper develops PeSeeD, a new metaheuristic algorithm for finding optimal spaced seed. Sequences matching is a hot topic in bio-informatics, which is used in many applications such as understanding the functional, structural, or evolutionary relationships between the sequences. The most relevant sequences matching methods are based on seeds designed to match two biological sequences. The first approach which introduced seeds was facilitated via Blastn tool, the approach builds seeds of 11 length size. However, it is clear that not all local alignments have to include an identical fragment of length 11. The spaced seeds approach is one of the methods which does not require a consecutive matching position. Dynamic programming is used to solve this kind of problem and it takes quadratic time. Several approaches have then been proposed to improve the sensitivity of searching in reasonable runtime. To reduce the complexity of such approaches, other heuristics based approaches can also be reviewed. The aim is to find spaced seeds subset which improves sensitivity without increasing the computation time. In this paper, the optimal subset spaced seeds are explored using the bio-inspired approach, penguins search optimisation algorithm (‘'PeSOA'' for short). The authors further propose an efficient heuristic for computing the overlap complexity between seeds. To evaluate the efficiency of the proposed approach, they compared the obtained results with the results of several seeds based software tools. The obtained results are very promising in terms of sensitivity and computation time for the overlap complexity.
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Dissertations / Theses on the topic "Bio-inspired optimisation method"

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Neshat, Mehdi. "The Application of Nature-inspired Metaheuristic Methods for Optimising Renewable Energy Problems and the Design of Water Distribution Networks." Thesis, 2020. http://hdl.handle.net/2440/130439.

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This work explores the technical challenges that emerge when applying bio-inspired optimisation methods to real-world engineering problems. A number of new heuristic algorithms were proposed and tested to deal with these challenges. The work is divided into three main dimensions: i) One of the most significant industrial optimisation problems is optimising renewable energy systems. Ocean wave energy is a promising technology for helping to meet future growth in global energy demand. However, the current technologies of wave energy converters (WECs) are not fully developed because of technical engineering and design challenges. This work proposes new hybrid heuristics consisting of cooperative coevolutionary frameworks and neuro-surrogate optimisation methods for optimising WECs problem in three domains, including position, control parameters, and geometric parameters. Our problem-specific algorithms perform better than existing approaches in terms of higher quality results and the speed of convergence. ii) The second part applies search methods to the optimization of energy output in wind farms. Wind energy has key advantages in terms of technological maturity, cost, and life-cycle greenhouse gas emissions. However, designing an accurate local wind speed and power prediction is challenging. We propose two models for wind speed and power forecasting for two wind farms located in Sweden and the Baltic Sea by a combination of recurrent neural networks and evolutionary search algorithms. The proposed models are superior to other applied machine learning methods. iii) Finally, we investigate the design of water distribution systems (WDS) as another challenging real-world optimisation problem. WDS optimisation is demanding because it has a high-dimensional discrete search space and complex constraints. A hybrid evolutionary algorithm is suggested for minimising the cost of various water distribution networks and for speeding up the convergence rate of search.
Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 2020
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Book chapters on the topic "Bio-inspired optimisation method"

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Bouarara, Hadj Ahmed. "Advanced Bioinspiration Methods for Optimisation Problems." In Advanced Bioinspiration Methods for Healthcare Standards, Policies, and Reform, 17–69. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-5656-9.ch002.

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Over the past decade, solving complex optimization problems with metaheuristics has received considerable attention among practitioners and researchers. As a result, many metaheuristic algorithms have been developed in recent years, and a large majority of these algorithms are often inspired by nature. Today, bio-inspired methods are becoming more and more popular. This popularity and success stems mainly from the fact that these algorithms were developed by mimicking nature's most efficient processes, including biological systems and physical and chemical processes. In this chapter, we will talk about metaheuristics and bio-inspired algorithms in general. From their basic concepts to their applications.
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