On the behaviour of differential evolution for problems with dynamic linear constraints

Evolutionary algorithms have been widely applied for solving dynamic constrained optimization problems (DCOPs) as a common area of research in evolutionary optimization. Current benchmarks proposed for testing these problems in the continuous spaces are either not scalable in problem dimension or the settings for the environmental changes are not flexible. Moreover, they mainly focus on non-linear environmental changes on the objective function. While the dynamism in some real-world problems exists in the constraints and can be emulated with linear constraint changes. The purpose of this paper is to introduce a framework which produces benchmarks in which a dynamic environment is created with simple changes in linear constraints (rotation and translation of constraint's hyperplane). Our proposed framework creates dynamic benchmarks that are flexible in terms of number of changes, dimension of the problem and can be applied to test any objective function. Different constraint handling techniques will then be used to compare with our benchmark. The results reveal that with these changes set, there was an observable effect on the performance of the constraint handling techniques.Maryam Hasani-Shoreh, Marìa-Yaneli Ameca-Alducin, Wilson Blaikie, Frank Neuman

Evolutionary dynamic constrained optimization: Test suite construction and algorithm comparisons

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Many real-world applications can be modelled as dynamic constrained optimization problems (DCOPs). Due to the fact that objective function and/or constraints change over time, solving DCOPs is a challenging task. Although solving DCOPs by evolutionary algorithms has attracted increasing interest in the community of evolutionary computation, the design of benchmark test functions of DCOPs is still insufficient. Therefore, we propose a test suite for DCOPs. A dynamic unconstrained optimization benchmark with good time-varying characteristics, called moving peaks benchmark, is chosen to be the objective function of our test suite. In addition, we design adjustable dynamic constraints, by which the size, number, and change severity of the feasible regions can be flexibly controlled. Furthermore, the performance of three dynamic constrained optimization evolutionary algorithms is tested on the proposed test suite, one of which is presented in this paper, named dynamic constrained optimization differential evolution (DyCODE). DyCODE includes three main phases: 1) the first phase intends to enter the feasible region from different directions promptly via a multi-population search strategy; 2) in the second phase, some excellent individuals chosen from the first phase form a new population to search for the optimal solution of the current environment; and 3) the third phase combines the memory individuals of the first two phases with some randomly generated individuals to re-initialize the population for the next environment. From the experiments, one can understand the strengths and weaknesses of the three compared algorithms for solving DCOPs in depth. Moreover, we also give some suggestions for researchers to apply these three algorithms on different occasions

Landscape-based Evolutionary Algorithms for Dynamic Optimization Problems

In real-world structured optimization problems, specific objective functions, decision variables, constraints, data and/or parameters may vary over time. These problems are generally recognized as dynamic optimization problems (DOPs). Evolutionary computation (EC) is a stochastic global search approach that has been successfully used to find optimal or near-optimal solutions for a wide range of optimization problems. EC is conceptually simple and imposes no specific mathematical properties requirement, thus showing competitive performance in dealing with static optimization problems. However, EC encounters challenges in dynamic problems on adaptability and efficiency. For the employment of EC in DOPs, two key points should be considered: the nature of optimization problems to be solved and the class of algorithms to be designed, where the crucial element of the former is landscape analysis and the latter frequently leads to the type of the algorithm. A new approach named Landscape Influenced Dynamic Optimization Algorithm (LIDOA) is proposed to incorporate landscape analysis information into the search process, where a landscape-based strategy is integrated with appropriately designed evolutionary algorithms. In LIDOA, the knowledge learned in each landscape is archived and re-utilized in the new environment. Several classical evolutionary algorithms, including genetic algorithm (GA), self-adaptive differential evolution algorithm (jDE) and covariance matrix adaptation evolution strategy (CMA-ES), are employed to examine the efficiency of LIDOA, and four landscape measures are considered. Experimental results showed the overall advantage of LIDOA. LIDOA with a single landscape measure is then expanded to multiple landscape measures. Three multi-measure methods are designed that are able to achieve good performance on evolutionary algorithms with appropriately integrated multiple landscape measures. According to the experimental results, LIDOA with multi-measure methods also improves the performance of GA, jDE and CMA-ES. The second key point in employing multiple evolutionary algorithms in DOPs is also studied. Three multi-algorithm methods are investigated based on jDE and GA, where an information sharing strategy and a self-adjusted parameter strategy are designed. Experimental results show that with an appropriate integration mechanism, all three multi-algorithm methods can obtain better performance over a single algorithm. Two key parameters in multi-algorithm methods are discussed. The similarity check strategy with multi-measure is also integrated with three multi-algorithm methods, and experimental results demonstrate the efficacy of both multi-algorithm methods and multi-measure strategies. Furthermore, to show the applicability of the concept in other algorithms, it is tested on quantum-inspired evolutionary algorithms. The performance of LIDOA with quantum-inspired evolutionary algorithms shows that LIDOA and quantum operators are beneficial for jDE, GA and CMA-ES, though their contributions vary. Finally, the proposed algorithms are applied to two practical problems (parameter estimation for frequency-modulated (FM) sound waves and spread spectrum radar polyphase code design). With appropriately selected landscape measure(s), LIDOA is able to improve the performance on both problems. When the complexity of the two applicable problems increases, the proposed hybrid framework with a multi-algorithm and multi-measure method is more reliable

Recommender Systems

Numerous problems in software coordination, operations research, manufacturing control and others can be transformed in constraint optimization problems (COPs). Moreover, most practical problems change constantly, requiring algorithms that can handle dynamic problems. When these problems are situated in a distributed setting, distributed algorithms are preferred or even necessary. In this paper we present the dynamic constraint optimization ant algorithm (DynCOAA) that can solve dynamic COPs. DynCOAA is specifically designed for dynamic problems, as it is based upon the ant colony optimization (ACO) meta-heuristic that has already proven its merit in other dynamic optimization problems. DynCOAA is a distributed algorithm that is suited for a one-on-one mapping between variables and hosts, but it can effectively accommodate multiple variables per host. We compared our algorithm to two existing algorithms for dynamic constraint optimization: DynAWC and DynDBA. We identify a category of problems where DynCOAA outperforms the other two algorithms, while it remains competitive for other categories of problems

Kennzahlenbasierte Steuerung, Koordination und Aktionsplanung in Multiagentensystemen

To be of practical use, the implementation of flexible and modular agent-based cyber-physical systems (CPS) for real-world autonomous control applications in Industry 4.0 oftentimes requires the domain-specific software agents to adhere to the organization's overall qualitative and quantitative business goals, usually expressed in terms of numeric key performance indicators (KPI). In this thesis, a general software framework for multi-agent systems (MAS) and CPS is developed that facilitates the integration and configuration of KPI-related objectives into the agents' individual decision processes. It allows the user of an agent system to define new KPIs and associated multi-criteria goals and supports inter-agent coordination as well as detailed KPI-based action planning, all at runtime of the MAS. The domain-independent components of the proposed KPI framework are implemented as a Java programming library and evaluated in a simulated production planning and control scenario

Which Dynamic Constraint Problems Can Be Solved By Ants?

There exist a number of algorithms that can solve dynamic constraint satisfaction/optimization problems (DynCSPs or DynCOPs). Because of the large variety in the characteristics of DynCSPs and DynCOPs, not all algorithms perfom equally well on all problems. In this paper, we present the Dynamic Constraint Optimization Ant Algorithm (DynCOAA). It is based upon the ant colony optimization (ACO) meta-heuristic that has already proven its merit in other dynamic optimization problems. We perform a large number of experiments to identify the dynamic constraint problems which our algorithm is most suited for. It turns out that this is a large class of problems, namely heterogeneous problems that change often. We find this to he common characteristics in real-world applications. For these problems. DynCOAA outperforms both the complete and non-complete traditional algorithms that were used for comparison. Copyright © 2006, American Association for Artificial Intelligence (www.aaai.org). All rights reserved.status: publishe

XXIII Edición del Workshop de Investigadores en Ciencias de la Computación : Libro de actas

Compilación de las ponencias presentadas en el XXIII Workshop de Investigadores en Ciencias de la Computación (WICC), llevado a cabo en Chilecito (La Rioja) en abril de 2021.Red de Universidades con Carreras en Informátic

The DynCOAA Algorithm for Dynamic Constraint Optimization Problems

research, manufacturing control and others can be transformed in constraint optimization problems (COPs). Moreover, most practical problems change constantly, requiring algorithms that can handle dynamic problems. When these problems are situated in a distributed setting, distributed algorithms are preferred or even necessary