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function optimization using an adaptive crossover operator based on locality

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 1997年第6期10卷 519-524页

作者： Ko, MS Kang, TW Hwang, CS Korea Univ Dept Comp Sci & Engn Seoul 136701 South Korea

This paper proposes a new crossover operator in genetic algorithms for function optimization. The proposed Adaptive Crossover Operator (ACO) restricts the crossover range by using a bias value. The bias_value is computed by the fitness function value, the performance ratio, and the number of generations. This ACO can reduce the computational complexity of obtaining the global optimum. By maintaining diversity in the population and sustaining the balance between exploration and exploitation, it can also prevent the genetic algorithm from getting stuck at a local optimum. As the generations progress, the ACO achieves a local improvement on a part of a chromosome which is restricted by the bias_value. The experiment demonstrates that a genetic algorithm using the ACO performs better than a genetic algorithm using a standard crossover operator in optimizing several functions. (C) 1998 Published by Elsevier Science Ltd. All rights reserved.

关键词： function optimization adaptive crossover operator bias_value local improvement

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function optimization algorithm based on SIRQV epidemic dynamic model

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JOURNAL OF COMPUTATIONAL SCIENCE 2015年 8卷 62-92页

作者： Huang, Guang-qiu Xian Univ Architecture & Technol Sch Management Xian 710055 Peoples R China

To solve some complicated function optimization problems, the SIRQV algorithm is constructed based on the SIRQV epidemic model. The algorithm supposes that some animal individuals exist in an ecosystem;each individual is characterized by a number of features;an infectious disease exists in the ecosystem and spreads among individuals, the disease attacks a part of features of an individual at each time. Each infected individual may pass through such states as susceptibility (S), infection (I), recovery (R), quarantine (Q) and vaccination (V), which can synthetically decide the physique strength of an individual. Individuals in the algorithm have 5 states such as S, I, R, Q and V, and 13 state transitions, each of which is equivalent to an operator. the 13 operators are logically organized together by the disease transmission logic of the SIRQV epidemic model so as to form a good cooperation and sufficient information exchange among individuals. The algorithm uses the activation, average, combination, reinforcement and assimilation operator to exchange feature information among individuals. The reinforcement operator transfers feature information from some strong individuals with higher individual physique index (IPI) to a weak individual with lower IPI index so as to make the latter grow better;the average operator ensures an individual to obtain average feature information from other individuals so as to reduce the probability that the individual drops into local optima;the activation operator expands an individual's search scope by increasing its vitality;the combination operator has the characteristics of both the activation operator and the average operator;the assimilation operator enables the search to possess of jumping ability along dimension direction;the REINIT operator has exploration and exploitation ability to overcome sticky state of individuals and enhance precision of global optima;the growth operator enables the algorithm to converge globally. Result

关键词： function optimization Intelligent optimization computation Epidemic dynamics SIRQV epidemic model SIRQV algorithm SIS algorithm

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function optimization by using genetic algorithms with individuals having different birth and survival rates

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ENGINEERING optimization 2001年第6期33卷 749-777页

作者： Mak, KL Wong, YS Univ Hong Kong Dept Ind & Mfg Syst Engn Hong Kong Hong Kong Peoples R China

This paper proposes an effective approach to function optimisation using the concept of genetic algorithms. The proposed approach differs from the canonical genetic algorithm in that the populations of candidate solutions consist of individuals from various age-groups, and each individual is incorporated with an age attribute to enable its birth and survival rates to be governed by predefined aging patterns. In order to ensure a stable search process, the condition that governs the relationships among the various birth and survival rates is determined. By generating the evolution of the populations with the genetic operators of selection, crossover and mutation, the proposed approach can provide excellent results by maintaining a better balance between exploitation and exploration of the solution space. A thorough study on the effects of the genetic parameters is carried out to examine the convergence behaviour of the proposed approach, and the findings illustrate how the convergence rate and the solution's quality are affected by the changes in the genetic parameters. The results of applying the proposed approach to solve five benchmark test problems are compared with those obtained by using the canonical genetic algorithm, Indeed, the proposed approach's performance is shown to surpass those of the canonical genetic algorithm.

关键词： function optimization genetic algorithms genetic operators

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function optimization via a Continuous Action-Set Reinforcement Learning Automata Model 4th

Function Optimization via a Continuous Action-Set Reinforcem...

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4th International Conference on Communications, Signal Processing, and Systems (CSPS)

作者： Guo, Ying Ge, Hao Wang, Fanming Huang, Yuyang Li, Shenghong Shanghai Jiao Tong Univ Dept Elect Engn Shanghai Peoples R China Riffle Inst Singapore Singapore Shanghai Starriver Bilingual Sch Shanghai Peoples R China

ISBN: (纸本)9783662498316;9783662498293

Learning automata as a tool for machine learning, could search the optimal state adaptively in random environment. function optimization is a fundamental issue and many practical models are ultimately the mathematical optimization problems. In this paper, we apply the basic continuous action-set reinforcement learning automata (CARLA) model to function optimization. An application model called equiCARLA is constructed by means of equidistant discretization and linear interpolation, and it presents a superiority over the existing algorithms not only in speed but also in precision. The experimental results demonstrate the effectiveness and efficiency of our model for function optimization.

关键词： function optimization CARLA Equidistant discretization Linear interpolation

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function optimization Using Robust Simulated Annealing 3rd

Function Optimization Using Robust Simulated Annealing

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3rd International Conference on Information System Design and Intelligent Applications (INDIA)

作者： Pandey, Hari Mohan Gajendran, Ahalya Amity Univ Dept Comp Sci & Engn Sect 125 Noida Uttar Pradesh India

ISBN: (纸本)9788132227571;9788132227564

In today's world, researchers spend more time in fine-tuning of algorithms rather than designing and implementing them. This is very true when developing heuristics and metaheuristics, where the correct choice of values for search parameters has a considerable effect on the performance of the procedure. Determination of optimal parameters is continuous engineering task whose goals are to reduce the production costs and to achieve the desired product quality. In this research, simulated annealing algorithm is applied to solve function optimization. This paper presents the application and use of statistical analysis method Taguchi design method for optimizing the parameters are tuned for the optimum output. The outcomes for various combinations of inputs are analyzed and the best combination is found among them. From all the factors considered during experimentation, the factors and its values which show the significant effect on output are discovered.

关键词： Simulated annealing algorithm function optimization Taguchi method Robust design

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Entanglement-Enhanced Quantum-Inspired Tabu Search Algorithm for function optimization

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IEEE ACCESS 2017年 5卷 13236-13252页

作者： Kuo, Shu-Yu Chou, Yao-Hsin Natl Chi Nan Univ Puli 54561 Taiwan Natl Chi Nan Univ Dept Comp Sci & Informat Engn Puli 54561 Taiwan

Many metaheuristic algorithms have been proposed to solve combinatorial and numerical optimization problems. Most optimization problems have high dependence, meaning that variables are strongly dependent on one another. If a method were to attempt to optimize each variable independently, its performance would suffer significantly. When traditional optimization techniques are applied to high-dependence problems, they experience difficulty in finding the global optimum. To address this problem, this paper proposes a novel metaheuristic algorithm, the entanglement-enhanced quantum-inspired tabu search algorithm (Entanglement-QTS), which is based on the quantum-inspired tabu search (QTS) algorithm and the feature of quantum entanglement. Entanglement-QTS differs from other quantum-inspired evolutionary algorithms in that its Q-bits have entangled states, which can express a high degree of correlation, rendering the variables more intertwined. Entangled Q-bits represent a state-of-the-art idea that can significantly improve the treatment of multimodal and high-dependence problems. Entanglement-QTS can discover optimal solutions, balance diversification and intensification, escape numerous local optimal solutions by using the quantum not gate, reinforce the intensification effect by local search and entanglement local search, and manage strong-dependence problems and accelerate the optimization process by using entangled states. This paper uses nine benchmark functions to test the search ability of the entanglement-QTS algorithm. The results demonstrate that Entanglement-QTS outperforms QTS and other metaheuristic algorithms in both its effectiveness at finding the global optimum and its computational efficiency.

关键词： Quantum-inspired tabu search (QTS) quantum entanglement metaheuristic algorithms function optimization

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An RNA evolutionary algorithm based on gradient descent for function optimization

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2024年第4期11卷 332-357页

作者： Wu, Qiuxuan Zhao, Zikai Chen, Mingming Chi, Xiaoni Zhang, Botao Wang, Jian Zhilenkov, Anton A. Chepinskiy, Sergey A. Hangzhou Dianzi Univ Int Joint Res Lab Autonomous Robot Syst Hangzhou 310018 Peoples R China Hangzhou Dianzi Univ HDU ITMO Joint Inst Hangzhou 310018 Peoples R China St Petersburg State Marine Tech Univ Inst Hydrodynam & Control Proc St Petersburg 190121 Russia Hangzhou Vocat & Tech Sch Jeely Automative Inst Hangzhou 310018 Peoples R China ITMO Univ Fac Control Syst & Robot St Petersburg 197101 Russia

The optimization of numerical functions with multiple independent variables was a significant challenge with numerous practical applications in process control systems, data fitting, and engineering designs. Although RNA genetic algorithms offer clear benefits in function optimization, including rapid convergence, they have low accuracy and can easily become trapped in local optima. To address these issues, a new heuristic algorithm was proposed, a gradient descent-based RNA genetic algorithm. Specifically, adaptive moment estimation (Adam) was employed as a mutation operator to improve the local development ability of the algorithm. Additionally, two new operators inspired by the inner-loop structure of RNA molecules were introduced: an inner-loop crossover operator and an inner-loop mutation operator. These operators enhance the global exploration ability of the algorithm in the early stages of evolution and enable it to escape from local optima. The algorithm consists of two stages: a pre-evolutionary stage that employs RNA genetic algorithms to identify individuals in the vicinity of the optimal region and a post-evolutionary stage that applies a adaptive gradient descent mutation to further enhance the solution's quality. When compared with the current advanced algorithms for solving function optimization problems, Adam RNA Genetic Algorithm (RNA-GA) produced better optimal solutions. In comparison with RNA-GA and Genetic Algorithm (GA) across 17 benchmark functions, Adam RNA-GA ranked first with the best result of an average rank of 1.58 according to the Friedman test. In the set of 29 functions of the CEC2017 suite, compared with heuristic algorithms such as African Vulture optimization Algorithm, Dung Beetle optimization, Whale optimization Algorithm, and Grey Wolf Optimizer, Adam RNA-GA ranked first with the best result of an average rank of 1.724 according to the Friedman test. Our algorithm not only achieved significant improvements over RNA-GA but also p

关键词： RNA-inspired operations adaptive gradient descent mutation operator heuristic algorithm function optimization

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Particle Swarm Optimizer with Aging Operator for Multimodal function optimization

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2013年第5期6卷 862-880页

作者： BoJiang Wang, Ning Li, Xiaodong Zhejiang Univ Natl Lab Ind Control Hangzhou 310027 Zhejiang Peoples R China RMIT Univ Sch Comp Sci & Informat Technol Melbourne Vic 3001 Australia

This paper proposes a new scheme for preventing a Particle Swarm Optimizer from premature convergence on multimodal optimization problems. Instead of only using fitness evaluation, we use a new index called particle age to guide population towards more promising region of the search space. The particle age is a measure of how long each particle moves towards a better solution. The main novelty of the proposed method is to let each particle learn from not only neighbours with better fitness values but also the neighbours whose fitness values are updated more frequently. To achieve this, we design a comprehensive age-based learning strategy, in which age is used for excluding old particles, selecting learning exemplars and deciding mutation strength and inertial weight for each particle. Experiments were conducted on 15 multimodal test functions to assess the performance of this new strategy in comparison with 7 state-of-the-art PSOs from the literature. The experimental results show the good performance of the proposed algorithm in solving multimodal functions when compared with several existing PSO variants.

关键词： soft computing function optimization particle swarm optimizer (PSO) swarm intelligence

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Artificial infectious disease optimization: A SEIQR epidemic dynamic model-based function optimization algorithm

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SWARM AND EVOLUTIONARY COMPUTATION 2016年 27卷 31-67页

作者： Huang, Guangqiu Xian Univ Architecture & Technol Sch Management Xian 710055 Peoples R China

To solve some complicated function optimization problems, an artificial infectious disease optimization algorithm based on the SEIQR epidemic model is constructed, it is called as the SEIQR algorithm, or SEIQRA in short. The algorithm supposes that some human individuals exist in an ecosystem;each individual is characterized by a number of features;an infectious disease (SARS) exists in the ecosystem and spreads among individuals, the disease attacks only a part of features of an individual. Each infected individual may pass through such states as susceptibility (S), exposure (E), infection (I), quarantine (Q) and recovery (R). State 5, E, I, Q and R can automatically and dynamically divide all people in the ecosystem into five classes, it provides the diversity for SEIQRA;that people can be attacked by the infectious disease and then transfer it to other people can cause information exchange among people, information exchange can make a person to transit from one state to another;state transitions can be transformed into operators of SEIQRA;the algorithm has 13 legal state transitions, which corresponds to 13 operators;the transmission rules of the infectious disease among people is just the logic to control state transitions of individuals among S, E, I, Q and R, it is just the synergy of SEIQRA, the synergy can be transformed into the logic structure of the algorithm. The 13 operators in the algorithm provide a native opportunity to integrate many operations with different purposes;these operations include average, differential, expansion, chevy, reflection and crossover. The 13 operators are executed equi-probably;a stable heart rhythm of the algorithm is realized. Because the infectious disease can only attack a small part of organs of a person when it spreads among people, the part variables iteration strategy (PVI) can be ingeniously applied, thus enabling the algorithm to possess of high performance of computation, high suitability for solving some kinds of

关键词： function optimization Intelligent optimization computation Epidemic dynamics SEIQR epidemic model Artificial infectious disease optimization

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An Improved Grey Wolf Optimizer Based on Tracking and Seeking Modes to Solve function optimization Problems

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IEEE ACCESS 2020年 8卷 69861-69893页

作者： Guo, M. W. Wang, J. S. Zhu, L. F. Guo, S. S. Xie, W. Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114051 Peoples R China

Grey wolf optimizer (GWO) is a new meta-heuristic algorithm. The GWO algorithm mimics the leadership hierarchy and hunting mechanism of grey wolves in nature. Three main stages of hunting include: encircling, tracking and attacking. It is easy to fall into local optimum when used to optimize high-dimensional data, and there is imbalance between exploration and exploitation. An improved grey wolf optimizer based on tracking mode and seeking mode is proposed to improve the diversity of the population and the ability of the algorithm to balance exploration and exploitation. The algorithm is verified by simulation experiments in three parts. Firstly, the proposed grey wolf optimizer based on tracking mode (TGWO), the improved grey wolf optimizer based on seeking mode (SGWO), the improved grey wolf optimizer based on tracking and seeking mode (TSGWO), Grey Wolf Optimizer (GWO), Particle Swarm optimization (PSO), Salp Swarm Algorithm (SSA), Sine Cosine Algorithm (SCA), Ant Lion Optimizer (ALO), Whale optimization Algorithm (WOA) and Moth-flame optimization (MFO) are adopted to optimize 21 typical benchmark functions respectively, and the obtained statistical simulation results are compared;Secondly, the improved algorithm proposed in this paper is compared with Binary Grey Wolf Optimizer (BGWO), Hybrid PSOGWO optimization (PSOGWO) and GWO Algorithm Integrated with Cuckoo Search (GWOCS);Finally, it is applied to the lightest design engineering problem of pressure vessels. Simulation results show that the superior performance of the proposed algorithm for exploiting the optimum and it has advantages in terms of exploration. The improved grey wolf optimizer based on tracking mode and seeking mode can better solve function optimization and classical engineering problems with constraints. It was found the improved grey wolf optimizer based on tracking mode has the high precision and the characteristics of balanced exploration and exploitation.

关键词： Grey wolf optimizer tracking mode seeking mode function optimization

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