Adaptive Evolution Algorithm Research Articles

Multiaperture multibeam antenna design method for high‐throughput satellite applications

As the core technology of high-throughput satellite payloads, multibeam antennas have received high attention and rapid development in recent years. Because of the characteristics of high gain and high beam isolation, multiaperture multibeam antennas is an important technical approach to meet the high-performance requirements of high-throughput satellites. This article proposes a mathematical modeling method based on the mutual coupling parameters of the reflector. This method takes capacity requirements and satellite structure design boundary conditions as input condition, and adopts an adaptive evolutionary multiobjective optimization algorithm to perform joint optimization and solution of multiple parameters, which can complete the optimization efficiently. This method does not need to perform a large number of antenna parameter simulation, reduces the number of iterations and structural layout design, greatly reduces the design cost. This article takes the demand of the AP-6D satellite as an example, applies the above method to the multibeam antenna design of the satellite, and completes the simulation verification, structural design, physical manufacturing, and various tests of the antenna to verify the correctness of the design method.

Growing and Evolving 3-D Prints

Design - especially of physical objects - can be understood as creative acts solving practical problems. In this paper we describe a biologically-inspired developmental model as the basis of a generative form-finding system. Using local interactions between cells in a two-dimensional environment, then capturing the state of the system at every time step, complex three-dimensional (3D) forms can be generated by the system. Unlike previous systems, our method is capable of directly producing 3D printable objects, eliminating intermediate transformations and manual manipulation often necessary to ensure the 3D form is printable. We devise fitness measures for optimising 3D printability and aesthetic complexity and use a Covariance Matrix Adaptation Evolutionary Strategies algorithm (CMA-ES) to find 3D forms that are both aesthetically interesting and physically printable using fused deposition modelling printing techniques. We investigate the system's capabilities by evolving and 3D printing objects at different levels of structural consistency, and assess the quality of the fitness measures presented to explore the design space of our generative system. We find that by evolving first for aesthetic complexity, then evolving for structural consistency until the form is 'just printable', gives the best results.

An adaptive disturbance multi-objective evolutionary algorithm based on decomposition

In solving multi-objective optimisation problems, the uniformly distributed weight vector of decomposition based multi-objective evolutionary algorithm (MOEA/D) is not completely suitable for the non-uniformly distributed Pareto front (PF). In order to solve the situation above, this paper proposes an adaptive disturbance multi-objective evolutionary algorithm based on decomposition (AD-MOEA/D), which introduces the disturbance individuals and disturbance weight vectors during the evolution. The disturbance individuals maintain the population diversity and improve convergence accuracy. The disturbance weight vectors assist the weight vectors to adjust adaptively and improve the distribution of PF. Besides, both disturbance individuals and disturbance weight vectors are produced according to the actual evolution, which will not participate in evolution when it is not necessary. The experimental results on multi-objective test functions show that the PF optimised by AD-MOEA/D has better convergence and distribution.

Adaptive Decomposition-Based Evolutionary Algorithm for Many-Objective Optimization with Two-Stage Dual-Density Judgment

Adaptive Decomposition-Based Evolutionary Algorithm for Many-Objective Optimization with Two-Stage Dual-Density Judgment

An adaptive disturbance multi-objective evolutionary algorithm based on decomposition

An adaptive disturbance multi-objective evolutionary algorithm based on decomposition

Robust multiobjective reservoir operation and risk decision-making model for real-time flood control coping with forecast uncertainty

Forecast uncertainty in real-time flood forecasting is the primary source of risk regarding reservoir flood control operation, affecting both reliability and safety of the entire system. Informing reservoir operation for real-time flood control coping with forecast uncertainty encourages overall robust control of risk probability, vulnerability, and resilience of upstream and downstream protection areas. Current stochastic programming models or chance constrained programming models (CCP) focuses on reducing either risk loss or vulnerability, thereby failing to ensure systematic control. This study established a robust multiobjective operation and risk decision-making model to inform reservoir operations. The proposed model includes three modules. First, a copula function was applied to capture multidimensional temporal dependences of forecast uncertainties, following which stochastic inflow scenarios were generated. Second, a robust multiobjective optimization (RMOO) model was established with the objectives of minimizing the probability of risk events, Conditional Value-at-Risk of risk loss (vulnerability) and the maximum sequential duration for both upstream and downstream areas. Then, an adaptive reference multiobjective evolutionary algorithm was used to solve the model and obtain noninferior solutions. Finally, the TOPSIS multicriteria decision-making method was introduced to select the compromise solution. Methodologies were verified by application to Xianghongdian reservoir, China. The main findings were as follows. (1) The copula function effectively captured the multidimensional positive temporal dependences of forecast uncertainties. (2) RMOO provided more widely distributed noninferior solutions than CCP, and performed better in lowering vulnerability of the system. (3) RMOO reduced the maximum release by 135.6 m3/s (7.33%) by increasing prerelease before the occurrence of flood peak. The proposed methodologies provide insights for supporting robust control of real-time operations of a reservoir system through simultaneous control of flood risk probability, vulnerability, and resilience.

Matching Biomedical Ontologies through Adaptive Multi-Modal Multi-Objective Evolutionary Algorithm

Simple SummaryBiomedical ontology matching is a large-scale multi-modal multi-objective optimization problem with sparse Pareto optimal solutions. To effectively address this challenging problem, this paper proposes an adaptive multi-modal multi-Objective Evolutionary Algorithm. First, a novel multi-objective optimization model is constructed to simultaneously optimize both the alignment’s f-measure and its conservativity. Then, a problem-specific algorithm is presented, which uses the guiding matrix to adaptively guide the algorithm’s convergence and diversity in both objective and decision spaces. The experimental results show that our approach is able to effectively solve the biomedical ontology matching problem and to provide more options for decision makers.To integrate massive amounts of heterogeneous biomedical data in biomedical ontologies and to provide more options for clinical diagnosis, this work proposes an adaptive Multi-modal Multi-Objective Evolutionary Algorithm (aMMOEA) to match two heterogeneous biomedical ontologies by finding the semantically identical concepts. In particular, we first propose two evaluation metrics on the alignment’s quality, which calculate the alignment’s statistical and its logical features, i.e., its f-measure and its conservativity. On this basis, we build a novel multi-objective optimization model for the biomedical ontology matching problem. By analyzing the essence of this problem, we point out that it is a large-scale Multi-modal Multi-objective Optimization Problem (MMOP) with sparse Pareto optimal solutions. Then, we propose a problem-specific aMMOEA to solve this problem, which uses the Guiding Matrix (GM) to adaptively guide the algorithm’s convergence and diversity in both objective and decision spaces. The experiment uses Ontology Alignment Evaluation Initiative (OAEI)’s biomedical tracks to test aMMOEA’s performance, and comparisons with two state-of-the-art MOEA-based matching techniques and OAEI’s participants show that aMMOEA is able to effectively determine diverse solutions for decision makers.

Wind farm layout optimization using adaptive evolutionary algorithm with Monte Carlo Tree Search reinforcement learning

Recent years have witnessed an enormous growth of wind farm capacity worldwide. Due to the wake effect, the velocity of incoming wind is reduced for the wind turbines in the downwind directions, thus causing discounted power generation in a wind farm. Previously, a self-informed adaptivity mechanism in evolutionary algorithms was introduced by the authors, which is inspired by the individuals’ self-adaptive capability to fit the environment in the natural world, where relocating the worst wind turbine with a surrogate model informed mechanism was found to be effective in improving the power conversion efficiency. In this paper, the exploitation capability in the adaptive genetic algorithm is further improved by casting the relocation of multiple wind turbines into a single-player reinforcement learning problem, which is further addressed by Monte-Carlo Tree Search embedded within the evolutionary algorithm. In contrast to the moderate improvements of the authors’ previous algorithms, significant improvement is achieved due to the enhanced algorithmic exploitation. The new algorithm is also applied to solve the optimal layout problem for a recently approved wind farm in New Jersey, and showed better performance against the benchmark algorithms.

Optimal reconfiguration of constellation using adaptive innovation driven multiobjective evolutionary algorithm

Constellation reconfiguration is a critical issue to recover from the satellite failure, maintain the regular operation, and enhance the overall performance. The constellation reconfiguration problem faces the difficulties of high dimensionality of design variables and extremely large decision space due to the great and continuously growing constellation size. To solve such real-world problems that can be hardly solved by traditional algorithms, the evolutionary operators should be promoted with available domain knowledge to guide the algorithm to explore the promising regions of the trade space. An adaptive innovationdriven multi-objective evolutionary algorithm (MOEA-AI) employing automated innovation (AI) and adaptive operator selection (AOS) is proposed to extract and apply domain knowledge. The available knowledge is extracted from the final or intermediate solution sets and integrated into an operator by the automated innovation mechanism. To prevent the overuse of knowledge-dependent operators, AOS provides top-level management between the knowledge-dependent operators and conventional evolutionary operators. It evaluates and selects operators according to their actual performance, which helps to identify useful operators from the candidate set. The efficacy of the MOEA-AI framework is demonstrated by the simulation of emergency missions. It was verified that the proposed algorithm can discover a non-dominant solution set with better quality, more homogeneous distribution, and better adaptation to practical situations.

A massively parallel speciation‐based differential evolution algorithm applied to the 3D‐AB protein structure prediction

AbstractOne of the most challenging problems in Bioinformatics is the finding of a protein conformation and it is known as the Protein Structure Prediction (PSP) problem. The main feature present in the AB off‐lattice model is the use of polarity as the main driving force to guide the optimization process. The present work proposes an adaptive evolutionary algorithm based on GPU that is applied to the 3D‐AB off‐lattice PSP problem. The proposed approach is named cuDSMjDE, and is composed of a Dynamic Speciation‐based Mutation Strategies Differential Evolution that employs the jDE mechanism to control F and CR parameters. A crowding strategy is also adopted, increasing competition between individuals and maintaining the diversity of solutions. All routines of the proposed algorithm are developed to run in GPU. During the experiments, eight Protein Data Bank sequences are employed. Results obtained concerning potential energy are competitive when compared with state‐of‐the‐art algorithms. The use of a massively parallel architecture promoted the necessary scalability with speedups up to 708.78.

A takeover time-driven adaptive evolutionary algorithm for mobile user tracking in pre-5G cellular networks

Cellular networks are one of today’s most popular means of communication. This fact has made the mobile phone industry subject to a huge scientific and economic competition, where the quality of service is key. Such a quality is measured on the basis of reliability, speed and accuracy when delivering a service to a user no matter his location or behaviour are. This fact has placed the users’ tracking process among the most difficult and determining issues in cellular network design. In this paper, we present an adaptive bi-phased evolutionary algorithm based on the takeover time to solve this problem. The proposal is thoroughly assessed by tackling twenty-five real-world instances of different sizes. Twenty-eight of the state-of-the-art techniques devised to address the users’ mobility problem have been taken as the comparison basis, and several statistical tests have been also conducted. Experiments have demonstrated that our solver outperforms most of the top-ranked algorithms.

Matching heterogeneous ontologies with adaptive evolutionary algorithm

An ontology provides a formal description on the domain concepts and their relationships. Due to the subjectivity of ontology engineers, one concept might be expressed in various ways, yielding the so-called ontology heterogeneity problem, and ontology matching is a ground method to address this problem. Ontology matching technique uses the similarity measure to determine the correspondences between two heterogeneous ontology entities. In order to improve the quality of ontology alignment, it is necessary to combine different kinds of similarity measures, and how to optimize the aggregating weights is called the ontology meta-matching problem. Tin this work, a heuristic evaluating metric on the ontology alignment is presented to evaluate the ontology alignment's quality, and a mathematical model on ontology meta-matching problem is constructed. Then, an Adaptive Evolutionary Algorithm (AEA) is proposed to effectively solve this problem. In particular, when the elite solution remains unchanged, AEA adaptively activates three independent exploring strategies, which, respectively use the adaptive selection, crossover and mutation operators based on the population diversity metric. In the experiment, we compare AEA among EA based matching technique and the state-of-the-art ontology matching technique, and the experimental results show its effectiveness.

Formal context reduction in deriving concept hierarchies from corpora using adaptive evolutionary clustering algorithm star

It is beneficial to automate the process of deriving concept hierarchies from corpora since a manual construction of concept hierarchies is typically a time-consuming and resource-intensive process. As such, the overall process of learning concept hierarchies from corpora encompasses a set of steps: parsing the text into sentences, splitting the sentences and then tokenising it. After the lemmatisation step, the pairs are extracted using formal context analysis (FCA). However, there might be some uninteresting and erroneous pairs in the formal context. Generating formal context may lead to a time-consuming process, so formal context size reduction is require to remove uninterested and erroneous pairs, taking less time to extract the concept lattice and concept hierarchies accordingly. In this premise, this study aims to propose two frameworks: (1) A framework to review the current process of deriving concept hierarchies from corpus utilising formal concept analysis (FCA); (2) A framework to decrease the formal context’s ambiguity of the first framework using an adaptive version of evolutionary clustering algorithm (ECA*). Experiments are conducted by applying 385 sample corpora from Wikipedia on the two frameworks to examine the reducing size of formal context, which leads to yield concept lattice and concept hierarchy. The resulting lattice of formal context is evaluated to the standard one using concept lattice-invariants. Accordingly, the homomorphic between the two lattices preserves the quality of resulting concept hierarchies by 89% in contrast to the basic ones, and the reduced concept lattice inherits the structural relation of the standard one. The adaptive ECA* is examined against its four counterpart baseline algorithms (Fuzzy K-means, JBOS approach, AddIntent algorithm, and FastAddExtent) to measure the execution time on random datasets with different densities (fill ratios). The results show that adaptive ECA* performs concept lattice faster than other mentioned competitive techniques in different fill ratios.

An adaptive multiobjective evolutionary algorithm based on grid subspaces

An adaptive multiobjective evolutionary algorithm based on grid subspaces

Multi-objective Adaptive Evolutionary Algorithm to Enhance Voltage Stability in Power Systems

Problems with two or more conflicting objectives have been handled as needing a multi-objective approach in recent years. The solution for these types of problems is normally to satisfy the conflicting objectives simultaneously in order to find tradeoffs between different criteria. Optimization in power systems is an important example of how to tackle multi-objective problems since these systems have conflicting performance indicators and normally operate close to their constraints due to the continuous increase in demand. In this paper, a multi-objective approach is applied to the voltage stability problem in power systems by using an adaptive evolutionary algorithm. The proposed method regards examining the following stability indicators of power systems: the voltage profile, the total reactive power loss, and the voltage collapse margin as requiring a multi-objective approach. Several experiments were conducted in IEEE 14, 57, and 118 busbar systems by using the proposed method and other probabilistic and heuristic optimization methods. The results showed that the proposed adaptive evolutionary algorithm enhanced the voltage stability and outperformed the other methods, especially when the size of the power system increases.

Matching large-scale biomedical ontologies with central concept based partitioning algorithm and Adaptive Compact Evolutionary Algorithm

As a unified model for describing biomedical knowledge, a biomedical ontology is of help to solve the issues of data heterogeneity in different biomedical databases. However, these ontologies might model same biomedical knowledge differently, yielding the heterogeneity problem. To address the biomedical ontology heterogeneity problem, it is necessary to match the heterogeneous concept pairs between two ontologies. How to reduce the computational complexity is a challenging problem when matching large-scale biomedical ontologies, which directly affects the matching efficiency and the alignment’s quality. To face this challenge, this work proposes a large-scale biomedical ontology partitioning and matching framework. In our proposal, a central concepts based ontology partitioning algorithm is first used to divide the ontology into several disjoint segments, which borrows the idea from the social network and Firefly Algorithm (FA). The proposed algorithm is able to partition the ontologies with low computation complexity, and at the same time, ensure the semantic completeness and the decent scale of each segment. Then, an Adaptive Compact Evolutionary Algorithm (ACEA) based matching technique is utilized to determine the ontology segment alignments, which can efficiently match the similar ontology segments. The experiment utilizes the biomedical testing cases provided by Ontology Alignment Evaluation Initiative (OAEI) to test our approach’s effectiveness, and the experimental results show that the alignments obtained by our method significantly outperforms the state-of-the-art biomedical ontology matching techniques.

Application of Optimization Algorithms in Clusters.

The structural characterization of clusters or nanoparticles is essential to rationalize their size and composition-dependent properties. As experiments alone could not provide complete picture of cluster structures, so independent theoretical investigations are needed to find out a detail description of the geometric arrangement and corresponding properties of the clusters. The potential energy surfaces (PES) are explored to find several minima with an ultimate goal of locating the global minima (GM) for the clusters. Optimization algorithms, such as genetic algorithm (GA), basin hopping method and its variants, self-consistent basin-to-deformed-basin mapping, heuristic algorithm combined with the surface and interior operators (HA-SIO), fast annealing evolutionary algorithm (FAEA), random tunneling algorithm (RTA), and dynamic lattice searching (DLS) have been developed to solve the geometrical isomers in pure elemental clusters. Various model or empirical potentials (EPs) as Lennard–Jones (LJ), Born–Mayer, Gupta, Sutton–Chen, and Murrell–Mottram potentials are used to describe the bonding in different type of clusters. Due to existence of a large number of homotops in nanoalloys, genetic algorithm, basin-hopping algorithm, modified adaptive immune optimization algorithm (AIOA), evolutionary algorithm (EA), kick method and Knowledge Led Master Code (KLMC) are also used. In this review the optimization algorithms, computational techniques and accuracy of results obtained by using these mechanisms for different types of clusters will be discussed.

An adaptive hybrid evolutionary algorithm and its application in aeroengine maintenance scheduling problem

An adaptive hybrid evolutionary algorithm and its application in aeroengine maintenance scheduling problem

Adaptive Multipopulation Evolutionary Algorithm for Contamination Source Identification in Water Distribution Systems

AbstractReal-time monitoring of drinking water in a water distribution system (WDS) can effectively warn of and reduce safety risks. One of the challenges is to identify the contamination source th...

Adaptive co-optimization of artificial neural networks using evolutionary algorithm for global radiation forecasting

Global radiation is not a regularly measured parameter in any weather station relative to other meteorological parameters due to measurement costs. This study has proposed hybrid artificial neural network models that predicted monthly radiation using typical weather and geographic data. Two datasets and six artificial neural network models were respectively built for indigenous and widespread regions around the world. The referred models co-optimized the artificial neural network properties and feature selection. For this purpose, an adaptive evolutionary algorithm improving prediction performance was developed to train the neural networks. This novel approach has yielded promising results compared to the developed deep learning models in this study. The results revealed that while the indigenous models had common features of longitude, sunshine durations, precipitation, and wind speed, the widespread models involved those of latitude, sunshine durations, and mean daily maximum air temperature. The proposed hybrid model had respectively the best mean absolute percentage errors of 2.45% and 9.93% for validation dataset and 3.75% and 11.03% for testing dataset of the indigenous and widespread regions, respectively. The present findings showed that the proposed hybrid model could be evaluated as a generic model and could improve the forecasting accuracy with the specified optimization parameters.