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Multi-tracker optimization Algorithm: A General Algorithm for Solving Engineering optimization problems

IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF MECHANICAL ENGINEERING 2017年第4期41卷 315-341页

作者： Zakeri, Ehsan Moezi, Seyed Alireza Bazargan-Lari, Yousef Zare, Amin Islamic Azad Univ Shiraz Branch Young Researchers & Elite Club Shiraz Iran Islamic Azad Univ Shiraz Branch Dept Mech Engn Shiraz Iran

In this paper, a new computational population-based optimization algorithm, which is designed based on the advantages and disadvantages of other evolutionary optimization algorithms introduced so far, is proposed. This new algorithm, which is named as "multi-tracker optimization algorithm," due to a multi-level structure of trackers within it, has some unique features, such as increasing the accuracy of the optimal point and continuous local search after convergence in order to escape from local minima simultaneously. Another important advantage of this algorithm is optimizing time-varying dynamical problems and tracking the optimal point. These characteristics make the algorithm very efficient for optimization problems, especially in the field of engineering. For a thorough investigation and comparison of this algorithm with other efficient optimization algorithms, different optimization problems such as static, dynamic, unconstrained and constrained, each of which has different challenges, are considered. The results of applying this algorithm on the abovementioned basic problems show the superiority of this algorithm over other efficient evolutionary algorithms.

关键词： MTOA Engineering optimization problems dynamic optimization problems

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Genetic algorithms with memory- and elitism-based immigrants in dynamic environments

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EVOLUTIONARY COMPUTATION 2008年第3期16卷 385-416页

作者： Yang, Shengxiang Univ Leicester Dept Comp Sci Leicester LE1 7RH Leics England

In recent years the genetic algorithm community has shown a growing interest in studying dynamic optimization problems. Several approaches have been devised. The random immigrants and memory schemes are two major ones. The random immigrants scheme addresses dynamic environments by maintaining the population diversity while the memory scheme aims to adapt genetic algorithms quickly to new environments by reusing historical information. This paper investigates a hybrid memory and random immigrants scheme, called memory-based immigrants, and a hybrid elitism and random immigrants scheme, called elitism-based immigrants, for genetic algorithms in dynamic environments. In these schemes, the best individual from memory or the elite from the previous generation is retrieved as the base to create immigrants into the population by mutation. This way, not only can diversity be maintained but it is done more efficiently to adapt genetic algorithms to the current environment. Based on a series of systematically constructed dynamic problems, experiments are carried out to compare genetic algorithms with the memory-based and elitism-based immigrants schemes against genetic algorithms with traditional memory and random immigrants schemes and a hybrid memory and multi-population scheme. The sensitivity analysis regarding some key parameters is also carried out. Experimental results show that the memory-based and elitism-based immigrants schemes efficiently improve the performance of genetic algorithms in dynamic environments.

关键词： genetic algorithms dynamic optimization problems memory random immigrants memory-based immigrants elitism-based immigrants

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A survey of multi-population optimization algorithms for tracking the moving optimum in dynamic environments

JOURNAL OF MEMBRANE COMPUTING

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JOURNAL OF MEMBRANE COMPUTING 2024年 1-23页

作者： Yazdani, Delaram Yazdani, Danial Blanco-Davis, Eduardo Nguyen, Trung Thanh Liverpool John Moores Univ Liverpool Logist Offshore & Marine LOOM Res Inst Fac Engn & Technol Liverpool L3 3AF England Univ Technol Sydney Fac Engn & Informat Technol Ultimo NSW 2007 Australia Liverpool John Moores Univ Liverpool Logist Offshore & Marine LOOM Res Inst Fac Engn & Technol Liverpool L2 2ER England

The solution spaces of many real-world optimization problems change over time. Such problems are called dynamic optimization problems (DOPs), which pose unique challenges that necessitate adaptive strategies from optimization algorithms to maintain optimal performance and responsiveness to environmental changes. Tracking the moving optimum (TMO) is an important class of DOPs where the goal is to identify and deploy the best-found solution in each environments Multi-population dynamic optimization algorithms are particularly effective at solving TMOs due to their flexible structures and potential for adaptability. These algorithms are usually complex methods that are built by assembling multiple components, each of which is responsible for addressing a specific challenge or improving the tracking performance in response to changes. This survey provides an in-depth review of multi-population dynamic optimization algorithms, focusing on describing these algorithms as a set of multiple cooperating components, the synergy between these components, and their collective effectiveness and/or efficiency in addressing the challenges of TMOs. Additionally, this survey reviews benchmarking practices within this domain and outlines promising directions for future research.

关键词： dynamic optimization problems Tracking the moving optimum Multi-population optimization algorithms Benchmarking

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Effective Streaming Evolutionary Feature Selection Using dynamic optimization 6th

Effective Streaming Evolutionary Feature Selection Using Dyn...

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6th IFIP International Conference on Computational Intelligence and Its Applications (CIIA)

作者： Boulesnane, Abdennour Meshoul, Souham Abdelhamid Mehri Constantine 2 Univ Coll NTIC Dept Comp Sci Constantine 25000 Algeria Abdelhamid Mehri Constantine 2 Univ MISC Lab Constantine Algeria

ISBN: (纸本)9783319897431;9783319897424

Feature selection is a key issue in machine learning and data mining. A great deal of effort has been devoted to static feature selection. However, with the assumption that features occur over time, methods developed so far are difficult to use if not applicable. Therefore, there is a need to design new methods to deal with streaming feature selection (SFS). In this paper, we propose the use of dynamic optimization to handle the dynamic nature of SFS with the ultimate goal to improve the quality of the evolving subset of selected features. A hybrid model is developed to fish out relevant features set as unnecessary by an online feature selection process. Experimental results show the effectiveness of the proposed framework compared to some state of the art methods.

关键词： dynamic optimization problems OSFS Feature selection Streaming features Classification

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Memory-based immigrants for genetic algorithms in dynamic environments 05

Memory-based immigrants for genetic algorithms in dynamic en...

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Genetic and Evolutionary Computation Conference

作者： Yang, Shengxiang Univ Leicester Dept Comp Sci Leicester LE1 7RH Leics England

ISBN: (纸本)1595930108

Investigating and enhancing the performance of genetic algorithms in dynamic environments have attracted a growing interest from the community of genetic algorithms in recent years. This trend reflects the fact that many real world problems are actually dynamic, which poses serious challenge to traditional genetic algorithms. Several approaches have been developed into genetic algorithms for dynamic optimization problems. Among these approches, random immigrants and memory schemes have shown to be beneficial in many dynamic problems. This paper proposes a hybrid memory and random immigrants scheme for genetic algorithms in dynamic environments. In the hybrid scheme, the best solution in memory is retrieved and acts as the base to create random immigrants to. replace the worst individuals in the population. In this way, not only can diversity be maintained but it is done more efficiently to adapt the genetic algorithm to the changing environment. The experimental results based on a series of systematically constructed dynamic problems show that the proposed memory-based immigrants scheme efficiently improves the performance of genetic algorithms in dynamic environments.

关键词： algorithms experimentation performance genetic algorithms dynamic optimization problems memory scheme random immigrants scheme

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Time-Dependent Ant Colony optimization Algorithm for Solving The Fastest Traffic Path Finding Problem in a dynamic Environment

Time-Dependent Ant Colony Optimization Algorithm for Solving...

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IEEE International Conference on Systems, Man, and Cybernetics (SMC)

作者： Elleuch, Wiam Wali, Ali Alimi, Adel M. Natl Engn Sch Sfax ENIS REGIM Lab Res Grp Intelligent Machines BP 1173 Sfax 3038 Tunisia

ISBN: (纸本)9781665442077

The goal of our project is to help drivers to preplan their trips and overcome the traffic congestion on roads. Unlike the traditional systems which provided the shortest path, travellers and salespersons need a reliable system to guide them to visit several cities or deliver merchandises in different locations in the shortest possible duration. Our system should deal with the different selected locations and get information about the congested roads and the travel time in a dynamic environment to estimate the fastest path. In this paper, we developed a system based on a time-dependent Ant Colony optimization (ACO) algorithm to solve the problem of finding the fastest traffic path instead of the shortest path. In addition, we implemented a dynamic real tests generator based on big Global Positioning System (GPS) datasets to get information about the traffic states in roads and generate dynamic problem instances. A mobile application has been developed to provide the drivers with accurate details of the path to visit the selected locations as well as the best time to begin the trip to avoid congestion and reach their destination as fast as possible. The exhaustive experimentations have shown the capabilities of the modified time-dependent ACO in solving dynamic optimization problems.

关键词： Time-Dependent Ant Colony optimization Fastest Path Finding Travel Salesman Problem Time-Dependent Pheromone Trails dynamic optimization problems

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Novel Associative Memory Retrieving Strategies for Evolutionary Algorithms in dynamic Environments

Novel Associative Memory Retrieving Strategies for Evolution...

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4th International Symposium on Intelligence Computation and Applications

作者： Cao, Yong Luo, Wenjian Univ Sci & Technol China Sch Comp Sci & Technol Nat Inspired Computat & Applicat Lab Hefei 230027 Anhui Peoples R China

ISBN: (纸本)9783642048425

Recently, Evolutionary Algorithms (EAs) with associative memory schemes have been developed to solve dynamic optimization problems (DOPs). Current associative memory schemes always retrieve both the best memory individual and the corresponding environmental information. However, the memory individual with the best fitness could not be the most appropriate one for new environments. In this paper, two novel associative memory retrieving strategies are proposed to obtain the most appropriate memory environmental information. In these strategies, two best individuals are first selected from the two best memory individuals and the Current best individual. Then, their corresponding environmental information is evaluated according to either the survivability or the diversity, one of which is retrieved. In experiments, the proposed two strategies were embedded into the state-of-the-art algorithm, i.e. the MPBIL and tested on three dynamic functions in cyclic environments. Experiment results demonstrate that the proposed retrieving strategies enhance the search ability in cyclic environments.

关键词： dynamic optimization problems evolutionary algorithms memory associative memory memory retrieving strategy

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A New Method to Optimize dynamic Environments with Global Changes Using the Chickens-Hen' Algorithm 15th

A New Method to Optimize Dynamic Environments with Global Ch...

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15th Mexican International Conference on Artificial Intelligence (MICAI)

作者： Zarei, Mostafa Parvin, Hamid Dadvar, Marzieh Islamic Azad Univ Dept Comp Engn Boushehr Branch Boushehr Iran

ISBN: (纸本)9783319624280;9783319624273

Many different methods for optimization have been proposed and they are swarm-based intelligence algorithms. By these types of algorithms optimized solutions can be found that almost close global solution. optimization algorithms both maximize and minimize issues to cover. optimization problems can be divided into two general categories: static and dynamic optimization. Most of the problems in the real world are dynamic in nature. Solving dynamic optimization problems due to the change in the optimal location would be very difficult. To solve dynamic optimization problems we should use an appropriate algorithm. Division algorithm chickens species and collective intelligence algorithms derived from nature is presented in this paper. This is a technically efficient algorithm for solving dynamic optimization based on the laws of probability and on the basis of the working population. This algorithm is an example of behaviorism in artificial intelligence. In this way, each member of the population who are called chickens with the implementation of individual and group behaviors are behaviors that proposed algorithm, they move towards the final answer. The algorithm for estimating the efficiency of a memory density with Euclidean clustering is used. Suitable solutions have been used to preserve the memory of the past. To test the efficiency of the proposed method of moving peaks benchmark famous simulator is used. Experimental results show that the proposed revision on moving peaks benchmark in dynamic performance is acceptable solve optimization problems.

关键词： Collective intelligence dynamic optimization problems Moving peaks benchmark

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Model-based optimization with Concept Drifts

Model-based Optimization with Concept Drifts

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Genetic and Evolutionary Computation Conference (GECCO)

作者： Richter, Jakob Shi, Junjie Chen, Jian-Jia Rahnenfuehrer, Joerg Lang, Michel TU Dortmund Univ Dortmund Germany

ISBN: (纸本)9781450371285

Model-based optimization (MBO) is a method to optimize expensive black-box functions that uses a surrogate to guide the search. We propose two practical approaches that allow MBO to optimize black-box functions where the relation between input and output changes over time, which are known as dynamic optimization problems (DOPs). The window approach trains the surrogate only on the most recent observations, and the time-as-covariate approach includes the time as an additional input variable in the surrogate, giving it the ability to learn the effect of the time on the outcomes. We focus on problems where the change happens systematically and label this systematic change concept drift. To benchmark our methods we define a set of benchmark functions built from established synthetic static functions that are extended with controlled drifts. We evaluate how the proposed approaches handle scenarios of no drift, sudden drift and incremental drift. The results show that both new methods improve the performance if a drift is present. For higher-dimensional multimodal problems the window approach works best and on lower-dimensional problems, where it is easier for the surrogate to capture the influence of the time, the time-as-covariate approach works better.

关键词： Model-based optimization Bayesian optimization dynamic optimization problems

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Multipopulation-Based Differential Evolution with Speciation-Based Response to dynamic Environments

Multipopulation-Based Differential Evolution with Speciation...

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4th International Conference on Swarm, Evolutionary, and Memetic Computing (SEMCCO)

作者： Kundu, Souvik Basu, Debabrota Chaudhuri, Sheli Sinha Jadavpur Univ Dept Elect & Telecommun Engn Kol 32 India

ISBN: (纸本)9783319037530;9783319037523

Unlike static optimization problems, the position, height and width of the peaks may vary with time instances in dynamic optimization problems (DOPs). Many real world problems are dynamic in nature. Evolutionary Algorithms (EAs) have been considered to solve the DOPs in the recent years. This article proposes a multi-population based Differential Evolution algorithm which uses a local mutation to control the perturbation of individuals and also avoid premature convergence. An exclusion rule is used to maintain the diversity in a subpopulation to cover a larger search space. Speciation-based memory archive has been used to utilize the previously found optimal information in the new change instance. Furthermore the proposed algorithm has been compared with four other state-of-the-art EAs over the Moving Peak Benchmark (MPB) problem and a benchmarks set named Generalized dynamic Benchmark Generator (GDBG) proposed for the 2009 IEEE Congress on Evolutionary Computation (CEC) competition.

关键词： Differential Evolution local mutation multi-population dynamic optimization problems speciation

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