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Designing Benchmark Generator for dynamic optimization Algorithm

IEEE ACCESS 2022年 10卷 638-648页

作者： Li, Hongjian Zhang, Geng Jishou Univ Sch Informat Sci & Engn Jishou 416000 Hunan Peoples R China Jishou Univ Sch Phys & Electromech Engn Jishou 416000 Hunan Peoples R China

Real-world problems always change over time. In the past two decades, many researchers have become increasingly interested in studying evolutionary optimization algorithms for dynamic problems owing to their great importance in real-world applications. Currently, there are no unified and mature test functions for dynamic problems, and the existing dynamic test functions are incapable of simulating a real dynamic environment. This study constructs a new benchmark test function generator for simulating dynamic environments that combine existing static benchmark functions and their related properties to form new dynamic test functions. It is simple to construct and conveniently control the dynamic properties by adjusting the corresponding parameters. Through a series of comparisons with the dynamic benchmark of the generalized dynamic benchmark generator (GDBG), the simulation results show that the dynamic test functions constructed by this generator can reflect different types of changing intensity.

关键词： Benchmark testing Generators optimization Heuristic algorithms Noise measurement Task analysis Logistics dynamic environment dynamic benchmark dynamic optimization problems changing intensity

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Towards a dynamic modeling of the predator prey problem

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APPLIED INTELLIGENCE 2016年第4期44卷 755-770页

作者： Ben-Romdhane, Hajer Alba, Enrique Krichen, Saoussen ISG Tunis LARODEC Lab 41 Rue Liberte Le Bardo Tunisia Univ Malaga Blvd Louis Pasteur S-N E-29071 Malaga Spain

This article addresses a new dynamic optimization problem (DOP) based on the Predator-Prey (PP) relationship in nature. Indeed, a PP system involves two adversary species where the predator's objective is to hunt the prey while the prey's objective is to escape from its predator. By analogy to dynamic optimization, a DOP can be seen as a predation between potential solution(s) in the search space, which represents the predator, and the moving optimum, as the prey. Therefore we define the dynamic predator-prey problem (DPP) whose objective is to keep track of the moving prey, so as to minimize the distance to the optimum. To solve this problem, a dynamic approach that continuously adapts to the changing environment is required. Accordingly, we propose a new evolutionary approach based on three main techniques for DOPs, namely: multi-population scheme, random immigrants, and memory of past solutions. This hybridization of methods aims at improving the evolutionary approaches ability to deal with DOPs and to restrain as much as possible their drawbacks. Our computational experiments show that the proposed approach achieves high performance for DPP and and competes with state of the art approaches.

关键词： Predator-prey model dynamic optimization problems Evolutionary algorithms

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A comparative study on the performance of dissortative mating and immigrants-based strategies for evolutionary dynamic optimization

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INFORMATION SCIENCES 2011年第20期181卷 4428-4459页

作者： Fernandes, C. M. Merelo, J. J. Rosa, A. C. Univ Granada Dept Comp Architecture E-18071 Granada Spain Univ Tecn Lisboa Biomed Engn & Evolutionary Syst Lab P-1049001 Lisbon Portugal

Traditional Genetic Algorithms (GAs) mating schemes select individuals for crossover independently of their genotypic or phenotypic similarities. In Nature, this behavior is known as random mating. However, non-random protocols, in which individuals mate according to their kinship or likeness, are more common in natural species. Previous studies indicate that when applied to GAs, dissortative mating - a type of non-random mating in which individuals are chosen according to their similarities - may improve their performance (on both speed and reliability). Dissortative mating maintains genetic diversity at a higher level during the run, a fact that is frequently observed as a possible cause of dissortative GAs' ability to escape local optima. dynamic optimization demands a special attention when designing and tuning a GA, since diversity plays an even more crucial role than it does when tackling static ones. This paper investigates the behavior of the Adaptive Dissortative Mating GA (ADMGA) in dynamic problems and compares it to GAs based on random immigrants. ADMGA selects parents according to their Hamming distance, via a self-adjustable threshold value. The method, by keeping population diversity during the run, provides an effective means to deal with dynamic problems. Tests conducted with dynamic trap functions and dynamic versions of Road Royal and knapsack problems indicate that ADMGA is able to outperform other GAs on a wide range of tests, being particularly effective when the frequency of changes is low. Specifically, ADMGA outperforms two state-of-the-art algorithms on many dynamic scenarios. In addition, and unlike preceding dissortative mating GAs and other evolutionary techniques for dynamic optimization, ADMGA self-regulates the intensity of the mating restrictions and does not increase the set of parameters in GAs, thus being easier to tune. (C) 2011 Elsevier Inc. All rights reserved.

关键词： Genetic Algorithms Dissortative mating dynamic optimization problems Random immigrants

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WD2O: a novel wind driven dynamic optimization approach with effective change detection

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APPLIED INTELLIGENCE 2017年第2期47卷 488-504页

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

dynamic optimization holds promise to solve real world problems that require adaptation to dynamic environments. The challenge is to track optima in an ever changing landscape. This paper describes a new computational intelligence approach to dynamic optimization termed as wind driven dynamic optimization (WD2O). Basically, it relies on an enhanced Multi-Region Modified Wind Driven optimization (MR-MWDO) model and exhibits four main features. First, a multi-region approach is used to classify regions of the search space into promising and non-promising areas with accordance to low and high pressure regions in the natural model. Second, it uses an effective collision avoidance strategy to prevent collision between sub-populations. Third, it allows cost effective change detection. Fourth, it maintains two types of populations in order to achieve better balanced search. The proposed WD2O has been successfully applied to Moving Peaks Benchmark (MPB) problem. An extensive experimental study has shown that WD2O outperforms significantly the first prototype MR-MWDO. Furthermore, it has shown very competitive results compared to state of the art methods and has achieved the best performance for high dimensional problems while keeping an appreciable time complexity.

关键词： dynamic optimization problems Swarm intelligence Multi-population Change detection Moving peaks benchmark

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Self-adaptive multi-population genetic algorithms for dynamic resource allocation in shared hosting platforms

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GENETIC PROGRAMMING AND EVOLVABLE MACHINES 2018年第4期19卷 505-534页

作者： Shirali, Azam Kordestani, Javidan Kazemi Meybodi, Mohammad Reza Islamic Azad Univ Dept Elect Comp & IT Engn Qazvin Branch Qazvin Iran Islamic Azad Univ Dept Elect Comp & IT Engn Sci & Res Branch Tehran Iran Amirkabir Univ Technol Tehran Polytech Soft Comp Lab Comp Engn & Informat Technol Dept 424 Hafez Ave Tehran Iran

This paper presents a self-adaptive multi-population approach based on genetic algorithm (GA) for solving dynamic resource allocation in shared hosting platforms. The proposed method, self-adaptive multi-population genetic algorithm (SAMPGA), is a multi-population GA strategy aimed at locating and tracking optima. This approach is based on preventing populations from searching in the same areas. Two adaptations to the basic approach are then proposed to further improve its performance. The first adapted algorithm, memory-based SAMPGA, is based on using explicit memory to store promising solutions and retrieve them upon detecting change in the environment. The second adapted algorithm, immigrants-based SAMPGA, is aimed at improving the technique used by SAMPGA to maintain a sustainable level of diversity needed for quick adaptation to the environmental changes. An extensive set of experiments is conducted on a variety of dynamic resource allocation scenarios, to evaluate the performance of the proposed approach. Results are also compared with those of self-organizing random immigrants GA using three well-known performance metrics. The experimental results indicate the effectiveness of the proposed approach.

关键词： Shared hosting platforms dynamic resource allocation Multi-population genetic algorithm dynamic optimization problems

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Adaptive and dynamic ant colony search algorithm for optimal distribution systems reinforcement strategy

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APPLIED INTELLIGENCE 2006年第1期24卷 31-42页

作者： Favuzza, S Graditi, G Sanseverino, ER Univ Palermo Dipartimento Ingn Elettr Elettron & Telecomunicaz I-90133 Palermo Italy Ente Nuove Tecnol Energia & Ambiente Ctr Ric ENEA Naples Italy

The metaheuristic technique of Ant Colony Search has been revised here in order to deal with dynamic search optimization problems having a large search space and mixed integer variables. The problem to which it has been applied is an electrical distribution systems management problem. This kind of issues is indeed getting increasingly complicated due to the introduction of new energy trading strategies, new environmental constraints and new technologies. In particular, in this paper, the problem of finding the optimal reinforcement strategy to provide reliable and economic service to customers in a given time frame is investigated. Utilities indeed need efficient software tools to take decisions in this new complex scenario. In past times, utilities project the load growth for several years and then estimate when the capacity limit will be exceeded. Designers then consider some feasible alternatives and select the optimal one in terms of performance and costs. In this paper, the Distributed Generation, DG, technology considered in compound solutions with the installation of feeder and substations is viewed as a new option for solving distribution systems capacity problems, along several years. The objective to be minimized is therefore the overall cost of distribution systems reinforcement strategy in a given timeframe. An application on a medium size network is carried out using the proposed technique that allows the identification of optimal paths in extremely large or non-finite spaces. The proposed algorithm uses an adaptive parameter in order to push exploration or exploitation as the search procedure stops in a local minimum. The algorithm allows the easy investigation of these kinds of complex problems, and allows to make useful comparisons as the intervention strategy and type of DG sources vary.

关键词： ant colony search dynamic optimization problems electrical distribution systems

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A single front genetic algorithm for parallel multi-objective optimization in dynamic environments

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NEUROCOMPUTING 2009年第16-18期72卷 3570-3579页

作者： Camara, Mario Ortega, Julio de Toro, Francisco Univ Granada Dept Comp Technol & Architecture E-18071 Granada Spain Univ Granada Dept Signal Theory Telemat & Commun E-18071 Granada Spain

This paper proposes a new parallel evolutionary procedure to solve multi-objective dynamic optimization problems along with some measures to evaluate multi-objective optimization in dynamic environments. These dynamic optimization problems appear in quite different real-world applications with actual socio-economic relevance. In these applications, the objective functions, the constraints, and hence, also the solutions, can change over time and usually demand to be solved online whilst the size of the changes is unknown. Although parallel processing could be very useful in these problems to meet the solution quality requirements and constraints, to date, not many parallel approaches have been reported in the literature. Taking this into account, we introduce a multi-objective optimization procedure for dynamic problems that are based on PSFGA, a parallel evolutionary algorithm previously proposed by us for multi-objective optimization. It uses an island model where a process divides the population among the remaining processes and allows the communication and coordination among the subpopulations in the different islands. The proposed algorithm makes an exclusive use of non-dominating individuals for the selection and variation operator and applies a crowding mechanism to maintain the diversity and the distribution of the solutions in the Pareto front. We also propose a model to understand the benefits of parallel processing in multi-objective problems and the speedup figures obtained in our experiments. (C) 2009 Elsevier B.V. All rights reserved.

关键词： Parallel multi-objective optimization dynamic optimization problems Parallel evolutionary algorithms Neuro-dynamic programming

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Performance evaluation of evolutionary heuristics in dynamic environments

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APPLIED INTELLIGENCE 2012年第1期37卷 130-144页

作者： Ayvaz, Demet Topcuoglu, Haluk Rahmi Gurgen, Fikret Bogazici Univ Dept Comp Engn Istanbul Turkey Marmara Univ Dept Comp Engn Istanbul Turkey

In recent years, there has been a growing interest in applying genetic algorithms to dynamic optimization problems. In this study, we present an extensive performance evaluation and comparison of 13 leading evolutionary algorithms with different characteristics on a common platform by using the moving peaks benchmark and by varying a set of problem parameters including shift length, change frequency, correlation value and number of peaks in the landscape. In order to compare solution quality or the efficiency of algorithms, the results are reported in terms of both offline error metric and dissimilarity factor, our novel comparison metric presented in this paper, which is based on signal similarity. Computational effort of each algorithm is reported in terms of average number of fitness evaluations and the average execution time. Our experimental evaluation indicates that the hybrid methods outperform the related work with respect to quality of solutions for various parameters of the given benchmark problem. Specifically, hybrid methods provide up to 24% improvement with respect to offline error and up to 30% improvement with respect to dissimilarity factor by requiring more computational effort than other methods.

关键词： dynamic optimization problems Evolutionary algorithms Performance evaluation

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Composite particle optimization with hyper-reflection scheme in dynamic environments

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APPLIED SOFT COMPUTING 2011年第8期11卷 4626-4639页

作者： Liu, Lili Wang, Dingwei Tang, Jiafu Northeastern Univ Sch Informat Sci & Engn Shenyang 110819 Peoples R China Northeastern Univ Minist Educ Key Lab Integrated Automat Proc Ind Shenyang 110819 Peoples R China

dynamic optimization is a challenging problem to the classic particle swarm optimization algorithms, it requires the optimizer not only to find the global optimal solution under a specific fitness landscape but also to track the trajectory of changing optima. This paper investigates a composite particle swarm optimizer, which presents a novel version of interactions among particles, to address dynamic optimization problems. A new composite particle generation approach based on the "fittest-oriented" principle is proposed, it creates each composite particle by one fitter particle from the swarm and other two particles randomly generated in its neighborhoods. In order to integrate valuable information for searching the changed optima, we introduce a scatter factor into the velocity-anisotropic reflection (VAR) scheme and a "fitness-and-distance" based pioneer particle identification (PPI) method. In addition, the composite particles interact with other particles in the swarm using an integral movement strategy, which aims to enhance the diversity of the swarm. Based on the experimental results in static landscapes, a hyper-reflection mechanism is introduced to enhance the efficiency of the VAR operator. Experimental results on the effect of the introduced schemes and user-specified parameters on DF1 problem provides a guideline for setting the involved parameters. Experimental comparisons with other state-of-art PSO variants and evolutionary computation algorithms on DF1 functions together with a suite of DOPs generated from the generalized dynamic benchmark generator (GDBG), which were used for the 2009 Competition on Evolutionary Computation in dynamic and Uncertain Environments (ECiDUE), are also provided. (C) 2011 Elsevier B.V. All rights reserved.

关键词： Particle swarm optimization Composite particle Velocity-anisotropic reflection dynamic optimization problems Hyper-reflection scheme

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An adaptive local search with prioritized tracking for dynamic Environments

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2015年第6期8卷 1053-1075页

作者： Masegosa, A. D. Onieva, E. Lopez-Garcia, P. Osaba, E. Perallos, A. Univ Deusto Deusto Inst Technol Bilbao 48007 Spain Basque Fdn Sci Ikerbasque Bilbao 48011 Spain

dynamic optimization problems (DOPs) have attracted a growing interest in recent years. This interest is mainly due to two reasons: their closeness to practical real conditions and their high complexity. The majority of the approaches proposed so far to solve DOPs are population-based methods, because it is usually believed that their higher diversity allows a better detection and tracking of changes. However, recent studies have shown that trajectory-based methods can also provide competitive results. This work is focused on this last type of algorithms. Concretely, it proposes a new adaptive local search for continuous DOPs that incorporates a memory archive. The main novelties of the proposal are two-fold: the prioritized tracking, a method to determine which solutions in the memory archive should be tracked first;and an adaptive mechanism to control the minimum step-length or precision of the search. The experimentation done over the Moving Peaks Problem (MPB) shows the benefits of the prioritized tracking and the adaptive precision mechanism. Furthermore, our proposal obtains competitive results with respect to state-of-the-art algorithms for the MPB, both in terms of performance and tracking ability.

关键词： dynamic Environments dynamic optimization problems Trajectory-based Methods Prioritized Tracking Local Search Adaptive Metaheuristics

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