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single-objective optimization with gain scheduling control

Single-objective optimization with gain scheduling control

IEEE IFAC International Conference on Automation (ICA) / 24th Congress of the Chilean-Association-of-Automatic-Control (ACCA)

作者： Marques, Tainara Reynoso-Meza, Gilberto Pontifical Catholic Univ Parana PUCPR Ind & Syst Engn Grad Program PPGEPS Curitiba Parana Brazil

ISBN: (纸本)9781665401272

This article presents a new method for adjusting the gain-scheduling controller, using the v-gap metric to reduce the number of gains in the gain-scheduling set and optimizing the proportional-integral-derivative (PID) values through single-objective optimization. For that, an algorithm was created with all the proposed steps, applying to a Peltier cell and comparing the results with two controllers: gain scheduling adjusted by a classical technique and PI controller adjusted with multi-objective optimization presented in [1]. With the reduced set, two new controllers were created: the first one optimizing a controller for each model in the set separately;the second one optimizing a single controller optimizing the whole set. As a result, it is observed that the separately optimized group is able to control almost as well as the gain scheduling adjusted by a classical technique and the PI controller tuning with multi-objective optimization, but both present a great effort in the control action. With a single controller, the response is slower, but the effort of the control action is significantly better.

关键词： Gain scheduling Peltier cell Proportion-Integral-Derivative (PID) controller single-objective optimization

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Landscape features in single-objective continuous optimization: Have we hit a wall in algorithm selection generalization?

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SWARM AND EVOLUTIONARY COMPUTATION 2025年 94卷

作者： Cenikj, Gjorgjina Petelin, Gasper Seiler, Moritz Cenikj, Nikola Eftimov, Tome Jozef Stefan Inst Comp Syst Dept Ljubljana 1000 Slovenia Jozef Stefan Int Postgrad Sch Ljubljana 1000 Slovenia Paderborn Univ D-33098 Paderborn Germany Tech Univ Munich D-80333 Munich Germany

The process of identifying the most suitable optimization algorithm fora specific problem, referred to as algorithm selection (AS), entails training models that leverage problem landscape features to forecast algorithm performance. A significant challenge in this domain is ensuring that AS models can generalize effectively to novel, unseen problems. This study evaluates the generalizability of AS models based on different problem representations in the context of single-objective continuous optimization. In particular, it considers the most widely used Exploratory Landscape Analysis features, as well as recently proposed Topological Landscape Analysis features, and features based on deep learning, such as DeepELA, TransOptAS and Doe2Vec. Our results indicate that when presented with out-of-distribution evaluation data, none of the feature-based AS models outperform a simple baseline model, i.e., a single Best Solver.

关键词： Algorithm selection optimization problem landscape features single-objective optimization Feature analysis

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DSM-DE: a differential evolution with dynamic speciation-based mutation for single-objective optimization

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MEMETIC COMPUTING 2020年第1期12卷 73-86页

作者： Deng, Libao Zhang, Lili Sun, Haili Qiao, Liyan Harbin Inst Technol Sch Informat Sci & Technol Weihai Shandong Peoples R China

A new differential evolution algorithm with two dynamic speciation-based mutation strategies (DSM-DE) is proposed to solve single-objective optimization problems. An explorative mutation "DE/seeds-to-seeds" and an exploitative mutation "DE/seeds-to-rand" are employed simultaneously in DSM-DE in the evolutionary process. A Dynamic Speciation Technique is designed to assist the two mutations in order to utilize the potential of selective portioning of critical individuals in the population. It dynamically divides the population into numbers of species whilst taking species seeds as centers. The best individuals for each species are used as base vectors in each species in the proposed mutation strategies. "DE/seeds-to-seeds" selects individuals from species seeds and current species to constitute difference vectors whereas "DE/seeds-to-rand" selects from the whole population. Thus the two mutation strategies can accelerate the convergence process without decreasing diversity of the population. Comparison results with four classic DE variants, one state-of-art DE variant and two improved non-DE variants on CEC2014, CEC2015 benchmark, and Lennard-Jones potential problem reveal that the overall performance of DSM-DE is better than that of the other seven DE algorithms. In addition, experiments also substantiate the effectiveness and superiority of two seeds-guided mutation strategies in DSM-DE.

关键词： Differential evolution Mutation strategy Dynamic speciation single-objective optimization

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Exploratory cuckoo search for solving single-objective optimization problems

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SOFT COMPUTING 2021年第15期25卷 10167-10180页

作者： Abed-alguni, Bilal H. Alawad, Noor Aldeen Barhoush, Malek Hammad, Rafat Yarmouk Univ Dept Comp Sci Irbid Jordan Yarmouk Univ Dept Comp Informat Syst Irbid Jordan

The cuckoo search (CS) algorithm is an effective optimization algorithm, but it is prone to stagnation in suboptimality because of some limitations in its exploration mechanisms. This paper introduces a variation of CS called exploratory CS (ECS), which incorporates three modifications to the original CS algorithm to enhance its exploration capabilities. First, ECS uses a special type of opposition-based learning called refraction learning to improve the ability of CS to jump out of suboptimality. Second, ECS uses the Gaussian perturbation to optimize the worst candidate solutions in the population before the discard step in CS. Third, in addition to the Levy flight mutation method used in CS, ECS employs two mutation methods, namely highly disruptive polynomial mutation and Jaya mutation, to generate new improved candidate solutions. A set of 14 widely used benchmark functions was used to evaluate and compare ECS to three variations of CS:CS with Levy flight (CS), CS with highly disruptive polynomial mutation (CS10) and CS with pitch adjustment mutation (CS11). The overall experimental and statistical results indicate that ECS exhibits better performance than all of the tested CS variations. Besides, the single-objective IEEE CEC 2014 functions were used to evaluate and compare the performance of ECS to six well-known swarm optimization algorithms: CS with Levy flight, Grey wolf optimizer (GWO), distributed Grey wolf optimizer (DGWO), distributed adaptive differential evolution with linear population size reduction evolution (L-SHADE), memory-based hybrid Dragonfly algorithm and Fireworks algorithm with differential mutation. Interestingly, the results indicate that ECS provides competitive performance compared to the tested six well-known swarm optimization algorithms.

关键词： Cuckoo search Refraction learning single-objective optimization Mutation method

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PaDE-NPC: Parameter Adaptive Differential Evolution With Novel Parameter Control for single-objective optimization

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IEEE ACCESS 2020年 8卷 139460-139478页

作者： Meng, Zhenyu Chen, Yuxin Li, Xiaoqing Lin, Fang Fujian Univ Technol Inst Artificial Intelligence Fuzhou 350000 Peoples R China

single objective real-parameter optimization problems exist in many areas of the real world, and Differential Evolution (DE) is a powerful population based stochastic optimization approach for tackling such problems. There are many different mutation strategies mentioned in the literature, and each of them has its own advantage. In this paper, we propose combined mutation strategies which can make a full use of the advantage of each mutation strategy regarding a population diversity indicator during the evolution. Furthermore, Novel Parameter Control (NPC) for the three control parameters including the scale factor F, crossover rate CR and population size PS are also proposed in the paper. Different from employing the fitness value as a weight in recent proposed state-of-the-art DE variants, our PaDE-NPC algorithm can tackle a large optimization problems especially for those the fitness differences are unavailable;Moreover, a platform based population size reduction scheme is also involved in the NPC, which can get a better perception of the landscape at the early stage of the evolution while obtaining a balance between exploration and exploitation in the later part of the evolution. The novel PaDE-NPC algorithm is verified under 58 benchmark functions from CEC2013 and CEC2017 test suits for real-parameter optimization competitions and experiment results show that our proposed PaDE-NPC algorithm outperforms these recently proposed powerful DE variants.

关键词： Combined strategy differential evolution novel parameter control single-objective optimization

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S/MIMO MC-CDMA Heuristic Multiuser Detectors Based on single-objective optimization

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WIRELESS PERSONAL COMMUNICATIONS 2010年第4期53卷 529-553页

作者： Abrao, Taufik de Oliveira, Leonardo D. Ciriaco, Fernando Angelico, Bruno A. Jeszensky, Paul Jean E. Casadevall Palacio, Fernando Jose State Univ Londrina UEL Dept Elect Engn DEEL BR-86051990 Londrina PR Brazil Univ Sao Paulo Escola Politecn Dept Telecommun & Control Engn LCS PTC BR-05508900 Sao Paulo Brazil Univ Politecn Cataluna Dept Signal Theory & Commun TSC ES-08034 Barcelona Spain

This paper analyzes the complexity-performance trade-off of several heuristic near-optimum multiuser detection (MuD) approaches applied to the uplink of synchronous single/multiple-input multiple-output multicarrier code division multiple access (S/MIMO MC-CDMA) systems. Genetic algorithm (GA), short term tabu search (STTS) and reactive tabu search (RTS), simulated annealing (SA), particle swarm optimization (PSO), and 1-opt local search (1-LS) heuristic multiuser detection algorithms (Heur-MuDs) are analyzed in details, using a single-objective antenna-diversity-aided optimization approach. Monte- Carlo simulations show that, after convergence, the performances reached by all near-optimum Heur-MuDs are similar. However, the computational complexities may differ substantially, depending on the system operation conditions. Their complexities are carefully analyzed in order to obtain a general complexity-performance framework comparison and to show that unitary Hamming distance search MuD (uH-ds) approaches (1-LS, SA, RTS and STTS) reach the best convergence rates, and among them, the 1-LS-MuD provides the best trade-off between implementation complexity and bit error rate (BER) performance.

关键词： MC-CDMA S/MIMO Multiuser detection Heuristic methods Tabu search Genetic algorithm Particle swarm optimization Simulated annealing Local search single-objective optimization Computational complexity

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Using multi-objective evolutionary algorithms for single-objective optimization

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4OR-A QUARTERLY JOURNAL OF OPERATIONS RESEARCH 2013年第3期11卷 201-228页

作者： Segura, Carlos Coello Coello, Carlos A. Miranda, Gara Leon, Coromoto CINVESTAV IPN Dpto Comp UMI LAFMIA CNRS 3175 Mexico City DF Mexico CINVESTAV IPN Dpto Comp Evolutionary Computat Grp Mexico City DF Mexico Univ La Laguna Dpto Estadist IO & Comp Tenerife Spain

In recent decades, several multi-objective evolutionary algorithms have been successfully applied to a wide variety of multi-objective optimization problems. Along the way, several new concepts, paradigms and methods have emerged. Additionally, some authors have claimed that the application of multi-objective approaches might be useful even in single-objective optimization. Thus, several guidelines for solving single-objective optimization problems using multi-objective methods have been proposed. This paper offers a survey of the main methods that allow the use of multi-objective schemes for single-objective optimization. In addition, several open topics and some possible paths of future work in this area are identified.

关键词： single-objective optimization Multi-objective optimization Constrained optimization Multiobjectivization Diversity preservation

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Multi-objective approaches in a single-objective optimization environment

Multi-objective approaches in a single-objective optimizatio...

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IEEE Congress on Evolutionary Computation

作者： Watanabe, S Sakakibara, K Ritsumeikan Univ Coll Informat Sci & Engn Shiga 5258577 Japan

ISBN: (纸本)0780393635

This paper presents two new approaches for transforming a single-objective problem into a multi-objective problem. These approaches add new objectives to a problem to make it multi-objective and use a multi-objective optimization approach to solve the newly defined problem. The first approach is based on relaxation of the constraints of the problem and the other is based on the addition of noise to the objective value or decision variable. Intuitively, these approaches provide more freedom to explore and a reduced likelihood of becoming trapped in local optima. We investigated the characteristics and effectiveness of the proposed approaches by comparing the performance on single-objective problems and multi-objective versions of those same problems. Through numerical examples, we showed that the multi-objective versions produced by relaxing constraints can provide good results and that using the addition of noise can obtain better solutions when the function is multimodal and separable.

关键词： constraint theory functions optimisation decision variable local optima multimodal function multiobjective optimization objective value problem constraint relaxation separable function single-objective optimization Separable function Constraint theory local optimum

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Tuning Maturity Model of Ecogeography-Based optimization On CEC 2015 single-objective optimization Test Problems

Tuning Maturity Model of Ecogeography-Based Optimization On ...

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IEEE Congress on Evolutionary Computation (CEC)

作者： Zheng, Yu-Jun Wu, Xiao-Bei Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310023 Zhejiang Peoples R China Tongji Univ Coll Elect & Informat Engn Shanghai 201804 Peoples R China

ISBN: (纸本)9781479974924

Ecogeography-based optimization (EBO) is an extension of biogeography-based optimization (BBO) that evolves a population of solutions by continually migrating features among them, mimicking the principle of immigration and emigration of species from one habitat to another in biogeographical distribution. The original EBO uses a linear maturity model for balancing exploration and exploitation. In this paper we generalize the maturity model by introducing a single control parameter, and then tune the parameter for each test problem of the CEC 2015 learning-based benchmark problem suite in order to find the most effective model for solving the problem. We design a binary search method for conveniently and effectively tuning the model. The computational experiments show that the tuned algorithm can improve the solution quality on different problems of the benchmark suite significantly.

关键词： Ecogeography-based optimization (EBO) maturity model parameter tuning single-objective optimization

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Characterization of CEC single-objective optimization Competition Benchmarks and Algorithms

Characterization of CEC Single-Objective Optimization Compet...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Misir, Mustafa Duke Kunshan University Division of Natural and Applied Sciences Kunshan China

ISBN: (纸本)9781665430654

The present study provides an analysis on the characteristics of single-objective optimization benchmark problems as well as the algorithms used to solve them. The target optimization domain involves the CEC competitions, each consisting a set of mathematical functions. Concerning the optimization tasks, the idea is to investigate the dis/-similarities between different competition scenarios and individual benchmarks. For the solvers, the goal is to detect the dis/-similarities between the algorithms applied to the CEC benchmarks. Those analysis missions are carried out by using the features directly and automatically extracted from the performance data, the quality of the solutions achieved by each algorithm on the benchmarks. The feature extraction process is realized through Singular Value Decomposition. Following the analysis on the algorithms, the potential of algorithm selection has been evaluated to see the performance improvement without actually developing a new algorithm, against those 20 algorithms. © 2023 IEEE.

关键词： Algorithm Selection single-objective optimization Singular Value Decomposition

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