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Parameter Tuning of a PID Controller Based on the cellular genetic algorithm

ENGINEERING LETTERS 2024年第4期32卷 828-834页

作者： Chen, Lifeng Jining Univ Expt Ctr Qufu 273155 Peoples R China

PID controller is the most widely used controller, and the performance of each controller depends on its three gain parameters. The genetic algorithm (GA) is an optimization process that involves three types of operators: selection, crossover, and mutation. Inspired by natural evolution, a GA searches for the optimum solution automatically from a collection of potential solutions. The GA was employed for the optimization of PID controller parameters in this paper;this algorithm evolves from generation to generation until the stopping criterion is met or the optimal solution is found. The traditional GA has several shortcomings, such as the great influence of the best individual on the whole population, poor global search ability, and premature convergence;therefore, an improved cellular genetic algorithm (CGA) was proposed whose genetic operators occur between the individual and its neighbors. CGA avoids the adverse influence of the best individual and the possibility of falling into a local optimum. The simulation results show that CGA outperforms traditional GA. CGA is more efficient than GA in global search activities, and its optimum solution is better than that of GA.

关键词： Index Terms-genetic algorithm cellular genetic algorithm Premature convergence Global search ability

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A Multifactorial cellular genetic algorithm for Multimodal Multitask Optimization

A Multifactorial Cellular Genetic Algorithm for Multimodal M...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Osaba, Eneko Del Ser, Javier Martinez, Aritz D. Lobo, Jesus L. Basque Res & Technol Alliance BRTA TECNALIA Derio Spain Univ Basque Country UPV EHU Bilbao Spain

ISBN: (数字)9781665467087

ISBN: (纸本)9781665467087

In multimodal optimization problems the main goal is to find as many global optima as possible by using a single search process. This type of optimization tasks emerges in many real-world scenarios in assorted fields including medicine, physics, and aerospace, among many others. However, addressing several multimodal optimization problems simultaneously has received little attention from the multitask optimization community to date. Even though solving different multimodal problems at the same time can largely benefit from the existing synergies among the modes of different tasks, this setup has been less studied than other optimization tasks. This work finds its inspiration in the incipient concepts of Evolutionary Multitasking and Multifactorial Optimization to propose a multifactorial cellular genetic algorithm for solving multimodal optimization problems. Our designed algorithm expedites the search for the global optima of different problems at a time by including several algorithmic steps aimed at adapting the search itself as per the synergies found over the exploration of the problems' landscape. An extensive experimentation has been designed using 14 different functions from the CEC'2013 competition on multimodal optimization benchmark. Besides evaluating the performance of the devised algorithm to retain the global optima of every function in the benchmark, we also conduct an analysis of the transfer of knowledge among such functions. Finally, we compare its performance to that of a winning proposal in this CEC'2013 competition so as to reflect on the suitability of the multitasking paradigm to solve multimodal optimization tasks.

关键词： Multimodal optimization Evolutionary Multitasking cellular genetic algorithm

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MO-MFCGA: Multiobjective Multifactorial cellular genetic algorithm for Evolutionary Multitasking

MO-MFCGA: Multiobjective Multifactorial Cellular Genetic Alg...

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IEEE Symposium Series on Computational Intelligence (IEEE SSCI)

作者： Osaba, Eneko Del Ser, Javier Martinez, Aritz D. Lobo, Jesus L. Nebro, Antonio J. Yang, Xin-She Basque Res & Technol Alliance BRTA TECNALIA Derio 48160 Bizkaia Spain Univ Basque Country UPV EHU Bilbao 48013 Bizkaia Spain Univ Malaga Dept Lenguajes & Ciencias Comp ITIS Software Malaga Spain Middlesex Univ London London NW4 4BT England

ISBN: (纸本)9781728190488

Multiobjetive optimization has gained a considerable momentum in the evolutionary computation scientific community. Methods coming from evolutionary computation have shown a remarkable performance for solving this kind of optimization problems thanks to their implicit parallelism and the simultaneous convergence towards the Pareto front. In any case, the resolution of multiobjective optimization problems (MOPs) from the perspective of multitasking optimization remains almost unexplored. Multitasking is an incipient research stream which explores how multiple optimization problems can be simultaneously addressed by performing a single search process. The main motivation behind this solving paradigm is to exploit the synergies between the different problems (or tasks) being optimized. Going deeper, we resort in this paper to the also recent paradigm Evolutionary Multitasking (EM). We introduce the adaptation of the recently proposed Multifactorial cellular genetic algorithm (MFCGA) for solving MOPs, giving rise to the Multiobjective MFCGA (MO-MFCGA). An extensive performance analysis is conducted using the Multiobjective Multifactorial Evolutionary algorithm as comparison baseline. The experimentation is conducted over 10 multitasking setups, using the Multiobjective Euclidean Traveling Salesman Problem as benchmarking problem. We also perform a deep analysis on the genetic transferability among the problem instances employed, using the synergies among tasks aroused along the MO-MFCGA search procedure.

关键词： Multiobjective Optimization Transfer Optimization Evolutionary Multitasking cellular genetic algorithm Traveling Salesman Problem

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A Memetic cellular genetic algorithm for Cancer Data Microarray Feature Selection

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IEEE LATIN AMERICA TRANSACTIONS 2020年第11期18卷 1874-1883页

作者： Rojas, Matias Gabriel Olivera, Ana Carolina Carballido, Jessica Andrea Vidal, Pablo Javier Univ Nacl Cuyo Fac Ingn UNCuyo CONICET Inst Univ Tecnol Informac & Comunicac Mendoza Argentina Univ Nacl Sur CONICET Inst Ciencias & Ingn Comp Bahia Blanca Buenos Aires Argentina

Gene selection aims at identifying a -small- subset of informative genes from the initial data to obtain high predictive accuracy for classification in human cancers. Gene selection can be considered as a combinatorial search problem and thus can be conveniently handled with optimization methods. This paper proposes a Memetic cellular genetic algorithm (MCGA) to solve the Feature Selection problem of cancer microarray datasets. Benchmark gene expression datasets, i.e., colon, lymphoma, and leukaemia available in the literature were used for experimentation. MCGA is compared with other well-known metaheuristic' strategies. The results demonstrate that our proposal can provide efficient solutions to find a minimal subset of the genes.

关键词： Feature Selection Microarray Classification cellular genetic algorithm Memetic algorithms

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Optimising Multilayer Perceptron weights and biases through a cellular genetic algorithm for medical data classification

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ARRAY 2022年 14卷

作者： Rojas, Matias Gabriel Olivera, Ana Carolina Vidal, Pablo Javier Univ Nacl Cuyo Inst Univ Tecnol Informac & Comunicac Consejo Nacl Invest Cient & Tecn Padre Jorge Contreras 1300M5502JMA Mendoza Mendoza Argentina Univ Nacl Cuyo Fac Ingn M5502JMA Mendoza Mendoza Argentina

In recent years, technology in medicine has shown a significant advance due to artificial intelligence becoming a framework to make accurate medical diagnoses. Models like Multilayer Perceptrons (MLPs) can detect implicit patterns in data, allowing identifying patients conditions that cannot be seen easily. MLPs consist of biased neurons arranged in layers, connected by weighted connections. Their effectiveness depends finding the optimal weights and biases that reduce the classification error, which is usually done by using the Back Propagation algorithm (BP). But BP has several disadvantages that could provoke the MLP not learn. Metaheuristics are alternatives to BP that reach high-quality solutions without using many computational resources. In this work, the cellular genetic algorithm (CGA) with a specially designed crossover operator called Damped Crossover (DX), is proposed to optimise weights and biases of the MLP to classify medical data. When compared against state-of-the-art algorithms, the CGA configured with DX obtained the minimal Mean Square Error value in three out of the five considered medical datasets and was the quickest algorithm with four datasets, showing a better balance between time consumed and optimisation performance. Additionally, it is competitive in enhancing classification quality, reaching the best accuracy with two datasets and second-best accuracy with two of the remaining.

关键词： Multilayer Perceptron Training methods cellular genetic algorithm Metaheuristics Medical data classification

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Emergency Resource Allocation for Multi-Period Post-Disaster Using Multi-Objective cellular genetic algorithm

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IEEE ACCESS 2020年 8卷 82255-82265页

作者： Wang, Feiyue Pei, Zhongwei Dong, Longjun Ma, Ju Cent South Univ Inst Disaster Prevent Sci & Safety Technol Changsha 410075 Peoples R China Cent South Univ Sch Resources & Safety Engn Changsha 410083 Peoples R China

As an important part of emergency response, the post-disaster emergency resource allocation is essential for mitigating disaster losses. To realize the effective allocation of relief materials and the reasonable selection of transportation routes, a multi-objective resource allocation model is proposed, considering the characteristics of uncertainty and persistence during rescue process. Furthermore, the multi-objective cellular genetic algorithm (MOCGA) is developed to solve the model by introducing the auxiliary population and neighborhood structure in the cellular automata. Finally, the comparison experiment proves that the overall performance of MOCGA is satisfactory compared with non-dominated multi-objective whale optimization algorithm (NSMOWOA), non-dominated multi-objective grey wolf optimizer (NSMOGWO) and non-dominated sorting genetic algorithm (NSGA-II) in the pareto front (PF), the hypervolume, the average value of objective function, and the PF ratio. Results show that MOCGA can solve the multi-objective dynamic emergency resource allocation model well, and can provide decision-makers with more excellent and diverse candidate rescue schemes than other algorithms. Besides, by analyzing the rescue schemes, this paper also provides a theoretical rescue scheme for decision-makers & x2019;scientific decisions.

关键词： cellular genetic algorithm emergency resource allocation multi-objective optimization disaster relief emergency logistics

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Multifactorial cellular genetic algorithm (MFCGA): algorithmic Design, Performance Comparison and genetic Transferability Analysis

Multifactorial Cellular Genetic Algorithm (MFCGA): Algorithm...

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IEEE Congress on Evolutionary Computation (CEC) as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Osaba, Eneko Martinez, Aritz D. Lobo, Jesus L. Del Ser, Javier Herrera, Francisco TECNALIA Basque Res & Technol Alliance BRTA Derio 48160 Bizkaia Spain Univ Basque Country UPV EHU Bilbao 48013 Bizkaia Spain Univ Granada DaSCI Andalusian Inst Data Sci & Computat Intelli Granada 18071 Spain

ISBN: (纸本)9781728169293

Multitasking optimization is an incipient research area which is lately gaining a notable research momentum. Unlike traditional optimization paradigm that focuses on solving a single task at a time, multitasking addresses how multiple optimization problems can be tackled simultaneously by performing a single search process. The main objective to achieve this goal efficiently is to exploit synergies between the problems (tasks) to be optimized, helping each other via knowledge transfer (thereby being referred to as Transfer Optimization). Furthermore, the equally recent concept of Evolutionary Multitasking (EM) refers to multitasking environments adopting concepts from Evolutionary Computation as their inspiration for the simultaneous solving of the problems under consideration. As such, EM approaches such as the Multifactorial Evolutionary algorithm (MFEA) has shown a remarkable success when dealing with multiple discrete, continuous, single-, and/or multi-objective optimization problems. In this work we propose a novel algorithmic scheme for Multifactorial Optimization scenarios - the Multifactorial cellular genetic algorithm (MFCGA) - that hinges on concepts from cellular Automata to implement mechanisms for exchanging knowledge among problems. We conduct an extensive performance analysis of the proposed MFCGA and compare it to the canonical MFEA under the same algorithmic conditions and over 15 different multitasking setups (encompassing different reference instances of the discrete Traveling Salesman Problem). A further contribution of this analysis beyond performance benchmarking is a quantitative examination of the genetic transferability among the problem instances, eliciting an empirical demonstration of the synergies emerged between the different optimization tasks along the MFCGA search process.

关键词： Transfer Optimization Evolutionary Multitasking cellular genetic algorithm Multifactorial Evolutionary algorithm Traveling Salesman Problem

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A Memetic cellular genetic algorithm for Multiple Sequence Alignment

A Memetic Cellular Genetic Algorithm for Multiple Sequence A...

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2020 Congress of Argentina

作者： Gabriel Rojas, Matfas Andrea Carballido, Jessica Carolina Olivera, Ana Javier Vidal, Pablo CONICET UNCuyo Inst Tec Info & Com ITIC Mendoza Argentina Univ Nacl Sur Inst Ciencias & Ingn Comp Bahia Blanca Buenos Aires Argentina Univ Nacl Cuyo CONICET Fac Ingn ITIC Mendoza Argentina

ISBN: (纸本)9781728159577

Multiple sequence alignment consists in the match of three or more sequences at the aim to discover both their shared and unshared areas. Multiple sequence alignment is frequently used for several activities of the biological sciences such as drugs finding, cancer study, and epidemiologic analysis. Thus it results fundamental. Nevertheless, the more sequences it has to align the more computational complexity it represents, so it is crucial to identify techniques that get good results at the same time they do good use of the resources. In this work, a memetic cellular genetic algorithm is presented to solve the multiple sequence alignment problem. The results have shown that our proposal can obtain good results keeping good performance in resources use.

关键词： multiple sequence alignment cellular genetic algorithm optimization bioinformatic

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GPU implementation of a cellular genetic algorithm for scheduling dependent tasks of physical system simulation programs

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JOURNAL OF COMBINATORIAL OPTIMIZATION 2018年第1期35卷 293-317页

作者： Zhao, Yan Chen, Liping Xie, Gang Zhao, Jianjun Ding, Jianwan Huazhong Univ Sci & Technol Dept Mech Sci & Engn Wuhan Hubei Peoples R China

This paper studies the problem of efficiently scheduling dependent computational tasks on heterogeneous computing systems. Computational tasks with precedence constraints are commonly represented by a directed acyclic graph (DAG). Four commonly used algorithms including a cellular genetic algorithm (CGA) are performed for scheduling a special type of DAGs derived from physical system simulation programs. Experimental results show that CGA outperforms the other three algorithms. However, when solving large instances of the dependent task scheduling problem, which are often the case for physical system simulation programs, a CPU implementation of the genetic algorithms can be extremely time consuming. The time complexity of producing a generation with a CPU is , where n is the size of DAG, and is the size of population. To improve runtimes, this paper presents a graphics processing unit (GPU) based implementation of the genetic algorithms. The time complexity of creating a new generation with a GPU is reduced to O(n). The experimental results show that significant speedups can be achieved by harnessing the power of a modern GPU.

关键词： Directed acyclic graph Heterogeneous scheduling cellular genetic algorithm GPU

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Addressing the 5G Cell Switch-off Problem with a Multi-objective cellular genetic algorithm 1

Addressing the 5G Cell Switch-off Problem with a Multi-objec...

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IEEE 5G World Forum (5GWF)

作者： Luna, Francisco Luque-Baena, Rafael M. Martinez, Jesus Valenzuela-Valdes, Juan F. Padilla, Pablo Univ Malaga Dept Languages & Comp Sci Malaga Spain Univ Granada Dept Signal Theory Telemat & Commun Granada Spain

ISBN: (纸本)9781538649824

The power consumption foreseen for SG networks is expected to be substantially greater than that of 4G systems, mainly because of the ultra-dense deployments required to meet the upcoming traffic demands. This paper deals with a multi-objective formulation of the Cell Switch-Off (CSO) problem, a well-known and effective approach to save energy in such dense scenarios, which is addressed with an accurate, yet rather unknown multi-objective metaheuristic called MOCell (multi-objective cellular genetic algorithm). It has been evaluated over a different set of networks of increasing densification levels. The results have shown that MOCell is able to reach major energy savings when compared to a widely used multi-objective algorithm.

关键词： Energy saving cell switch-off multi-objective optimization metaheuristics cellular genetic algorithm

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