Compact Differential Evolution Research Articles

FPGA-Based Compact Differential Evolution for General-Purpose Optimization in Resource-Constrained Devices

FPGA-Based Compact Differential Evolution for General-Purpose Optimization in Resource-Constrained Devices

Matching Sensor Ontologies With Multi-Context Similarity Measure and Parallel Compact Differential Evolution Algorithm

Sensor ontology is able to resolve the sensor data heterogeneity issue among the Cybertwin-driven 6G based Internet of Everything (IoE) systems. However, due to human subjectivity, the sensor ontologies also suffer from the heterogeneity problem. To address this problem, it is necessary to execute the sensor Ontology Matching (OM) process, i.e., finding the identical entity pairs between two ontologies. To this end, we first propose a Multi-Context based ESM (MC-ESM) to measure the similarity of two entities by taking into consideration their semantic contexts. After that, a parallel compact Differential Evolution with Adaptive Step Length (pcDE-ASL) is proposed to find all entity mappings, which uses the probability presentation on the population to save the memory consumption, and the parallel processing mechanism and ASL to help the algorithm efficiently converge on the global optima. The experimental results show that pcDE-ASL is both effective and efficient.

A compact compound sinusoidal differential evolution algorithm for solving optimisation problems in memory-constrained environments

In this paper, a new compact algorithm is proposed. Two sinusoidal formulas are used to automatically adjust the crossover rate and the mutation scaling factor in the compact Differential Evolution (cDE) metaheuristic. The proposed algorithm, called Compound Sinusoidal cDE, CScDE, is compared to seven state-of-the-art compact algorithms on the well-known BBOB test-bed, the CEC-2014 test suite for continuous optimisation, as well as five real-world optimisation problems chosen from the CEC-2011 benchmarks. The CScDE algorithm outperformed its competitors for most problem categories and over most dimensions. It is also compared to some well-established population-based metaheuristics.

Matching biomedical ontologies through Compact Differential Evolution algorithm with compact adaption schemes on control parameters

Biomedical ontology is a unified model for describing biomedical knowledge, which can be of help to solve the issues of heterogeneity in different biomedical databases. However, the existing biomedical ontologies could define the same biomedical concept in different ways, which yields the biomedical ontology heterogeneous problem. To implement the inter-operability among the biomedical ontologies, it is critical to establish the semantic links between heterogenous biomedical concepts, so-called biomedical ontology matching. Evolution Algorithm (EA) is a state-of-the-art methodology for matching ontologies, but two main shortcomings, i.e. the huge memory consumption and long runtime, make it incapable of effectively matching biomedical ontologies. In this work, a novel Adaptive Compact Differential Evolution algorithm (ACDE) is proposed to solve the biomedical ontology matching problem, which utilizes a compact encoding mechanism to save the memory consumption and introduces the compact adaption schemes on control parameters to improve the algorithm’s converging speed. The experiment exploits four biomedical ontology matching tracks, which are provided by the famous Ontology Alignment Evaluation Initiative (OAEI), to test ACDE’s performance. The experimental results show that ACDE can effectively reduce EA-based ontology matcher’s memory consumption and runtime, and its results significantly outperform other EA-based matchers and OAEI’s participants.

Parallel Compact Differential Evolution for Optimization Applied to Image Segmentation

A parallel compact Differential Evolution (pcDE) algorithm is proposed in this paper. The population is separated into multiple groups and the individual is run by using the method of compact Differential Evolution. The communication is implemented after predefined iterations. Two communication strategies are proposed in this paper. The first one is to replace the local optimal solution by global optimal solution in all groups, which is called optimal elite strategy (oe); the second one is to replace the local optimal solution by mean value of the local optimal solution in all groups, which is called mean elite strategy (me). Considering that the pcDE algorithm does not need to store a large number of solutions, the algorithm can adapt to the environment with weak computing power. In order to prove the feasibility of pcDE, several groups of comparative experiments are carried out. Simulation results based on the 25 test functions demonstrate the efficacy of the proposed two communication strategies for the pcDE. Finally, the proposed pcDE is applied to image segmentation and experimental results also demonstrate the superior quality of the pcDE compared with some existing methods.

Matching biomedical ontologies through compact differential evolution algorithm

ABSTRACTAlthough biomedical ontologies have been widely used in the life science domain, the heterogeneous problem among biomedical ontologies hampers their inter-operability. Thus, the establishment of meaningful links between heterogeneous biomedical ontologies, so-called biomedical ontology matching, is critical to the success of biomedical ontology engineering. To determine the biomedical ontology alignment with high quality, in this work, a Hybrid Compact Differential Evolution (HCDE) algorithm-based biomedical ontology matching technique is proposed. In particular, we propose a similarity metric on biomedical concepts, construct an optimal model for the biomedical ontology matching problem, and introduce a binomial crossover into CDE's evolving the process to enhance its performance. The experiments are carried out on the Disease and Phenotype track and Biodiversity and Ecology track from the Ontology Alignment Evaluation Initiative (OAEI 2018). The experimental results show that HCDE can significantly improve the CDE in terms of the alignment's quality, and the alignments obtained by HCDE are also better than OAEI 2018's participants.

Interactive Cross-Lingual Ontology Matching

Recently, with the growing number of ontologies defined in different languages, to bridge the semantic gaps between them, it is necessary to identify the correspondences between their heterogeneous entities, so-called cross-lingual ontology matching. Due to the complexity and the intricacy of the cross-lingual ontology matching, it is essential to get an expert involved in the matching process to guarantee the alignment's quality. In this paper, we propose an interactive cross-lingual ontology matching technique that makes the user and automatic matcher work together to create high-quality alignments in a reasonable amount of time. In particular, we present a cross-lingual similarity metric to calculate the similarity value of two cross-lingual entities, construct an optimal model for the cross-lingual ontology matching problem, and propose an interactive compact differential evolution (ICDE) algorithm to effectively match the cross-lingual ontologies. The experiment exploits the ontology alignment evaluation initiative (OAEI) multifarm track to test our proposal's performance. The experimental results show that the ICDE significantly outperforms other EA-based matchers and OAEI's participants, and the interacting mechanism can significantly improve the alignment's quality.

Automated Functional Test Generation for Digital Systems Through a Compact Binary Differential Evolution Algorithm

At present, the functional verification of a device represents the highest cost during manufacturing. To reduce that cost, several methods have been suggested. In this work we propose a method which produces a set of binary test sequences by means of a Compact binary Differential Evolution algorithm (Compact-BinDE). The strategy employed is based on the use of coverage models and cost functions in the verification process, which are built with relevant conditions or coverage points representing the full device behavior. The main problem is to cover all difficult situations since the relationships between the test points and the input data in the design are not trivial. The test generation method is included with a proposed verification platform based on a simulation representing a hybrid method. The main contribution of this work is that the method obtains test vector sequences that maximize the coverage percentage on run-time device simulation with an efficient search in the binary domain. Also, different to the previous works that used meta-heuristics, the proposed method by means of the Compact-BinDE algorithm can reduce the simulation time used to obtain test sequences that exercise the coverage points. The results show that the proposed method represents a good alternative to generate test sequences to cover the coverage points during the functional verification.

On the Use of Compact Approaches in Evolution Strategies

Compact evolutionary algorithms have proven to be an efficient alternative for solving optimization problems in computing environments with low processing power. In this kind of solution, a probability distribution simulates the behavior of a population, thus looking for memory savings. Several compact algorithms have been proposed, including the compact genetic algorithm and compact differential evolution. This work aims to investigate the use of compact approaches in other important evolutionary algorithms: evolution strategies. This paper proposes two different approaches for compact versions of evolution strategies. Experiments were performed and the results analyzed. The results showed that, depending on the nature of problem, the use of the compact version of Evolution Strategies can be rewarding.

Compact Differential Evolution Light: High Performance Despite Limited Memory Requirement and Modest Computational Overhead

Compact algorithms are Estimation of Distribution Algorithms which mimic the behavior of population-based algorithms by means of a probabilistic representation of the population of candidate solutions. These algorithms have a similar behaviour with respect to population-based algorithms but require a much smaller memory. This feature is crucially important in some engineering applications, especially in robotics. A high performance compact algorithm is the compact Differential Evolution (cDE) algorithm. This paper proposes a novel implementation of cDE, namely compact Differential Evolution light (cDElight), to address not only the memory saving necessities but also real-time requirements. cDElight employs two novel algorithmic modifications for employing a smaller computational overhead without a performance loss, with respect to cDE. Numerical results, carried out on a broad set of test problems, show that cDElight, despite its minimal hardware requirements, does not deteriorate the performance of cDE and thus is competitive with other memory saving and population-based algorithms. An application in the field of mobile robotics highlights the usability and advantages of the proposed approach.

Noise analysis compact differential evolution

This article proposes a compact algorithm for optimisation in noisy environments. This algorithm has a compact structure and employs differential evolution search logic. Since it is a compact algorithm, it does not store a population of solutions but a probabilistic representation of the population. This kind of algorithmic structure can be implemented in those real-world problems characterized by memory limitations. The degree of randomization contained in the compact structure allows a robust behaviour in the presence of noise. In addition the proposed algorithm employs the noise analysis survivor selection scheme. This scheme performs an analysis of the noise and automatically performs a re-sampling of the solutions in order to ensure both reliable pairwise comparisons and a minimal cost in terms of fitness evaluations. The noise analysis component can be reliably used in noise environments affected by Gaussian noise which allow an a priori analysis of the noise features. This situation is typical of problems where the fitness is computed by means of measurement devices. An extensive comparative analysis including four different noise levels has been included. Numerical results show that the proposed algorithm displays a very good performance since it regularly succeeds at handling diverse fitness landscapes characterized by diverse noise amplitudes.

Disturbed Exploitation compact Differential Evolution for limited memory optimization problems

This paper proposes a novel and unconventional Memetic Computing approach for solving continuous optimization problems characterized by memory limitations. The proposed algorithm, unlike employing an explorative evolutionary framework and a set of local search algorithms, employs multiple exploitative search within the main framework and performs a multiple step global search by means of a randomized perturbation of the virtual population corresponding to a periodical randomization of the search for the exploitative operators. The proposed Memetic Computing approach is based on a populationless (compact) evolutionary framework which, instead of processing a population of solutions, handles its statistical model. This evolutionary framework is based on a Differential Evolution which cooperatively employs two exploitative search operators: the first is based on a standard Differential Evolution mutation and exponential crossover, and the second is the trigonometric mutation. These two search operators have an exploitative action on the algorithmic framework and thus contribute to the rapid convergence of the virtual population towards promising candidate solutions. The action of these search operators is counterbalanced by a periodical stochastic perturbation of the virtual population, which has the role of “disturbing” the excessively exploitative action of the framework and thus inhibits its premature convergence. The proposed algorithm, namely Disturbed Exploitation compact Differential Evolution, is a simple and memory-wise cheap structure that makes use of the Memetic Computing paradigm in order to solve complex optimization problems. The proposed approach has been tested on a set of various test problems and compared with state-of-the-art compact algorithms and with some modern population based meta-heuristics. Numerical results show that Disturbed Exploitation compact Differential Evolution significantly outperforms all the other compact algorithms present in literature and reaches a competitive performance with respect to modern population algorithms, including some memetic approaches and complex modern Differential Evolution based algorithms. In order to show the potential of the proposed approach in real-world applications, Disturbed Exploitation compact Differential Evolution has been implemented for performing the control of a space robot by simulating the implementation within the robot micro-controller. Numerical results show the superiority of the proposed algorithm with respect to other modern compact algorithms present in literature.

Compact Differential Evolution

This paper proposes the compact differential evolution (cDE) algorithm. cDE, like other compact evolutionary algorithms, does not process a population of solutions but its statistic description which evolves similarly to all the evolutionary algorithms. In addition, cDE employs the mutation and crossover typical of differential evolution (DE) thus reproducing its search logic. Unlike other compact evolutionary algorithms, in cDE, the survivor selection scheme of DE can be straightforwardly encoded. One important feature of the proposed cDE algorithm is the capability of efficiently performing an optimization process despite a limited memory requirement. This fact makes the cDE algorithm suitable for hardware contexts characterized by small computational power such as micro-controllers and commercial robots. In addition, due to its nature cDE uses an implicit randomization of the offspring generation which corrects and improves the DE search logic. An extensive numerical setup has been implemented in order to prove the viability of cDE and test its performance with respect to other modern compact evolutionary algorithms and state-of-the-art population-based DE algorithms. Test results show that cDE outperforms on a regular basis its corresponding population-based DE variant. Experiments have been repeated for four different mutation schemes. In addition cDE outperforms other modern compact algorithms and displays a competitive performance with respect to state-of-the-art population-based algorithms employing a DE logic. Finally, the cDE is applied to a challenging experimental case study regarding the on-line training of a nonlinear neural-network-based controller for a precise positioning system subject to changes of payload. The main peculiarity of this control application is that the control software is not implemented into a computer connected to the control system but directly on the micro-controller. Both numerical results on the test functions and experimental results on the real-world problem are very promising and allow us to think that cDE and future developments can be an efficient option for optimization in hardware environments characterized by limited memory.

Composed compact differential evolution

This paper proposes a novel algorithm for solving continuous complex optimization problems with a relatively low memory consumption. The proposed approach, namely Composed compact Differential Evolution, consists of a set of compact Differential Evolution units which simultaneously search the decision space from various perspectives. A randomization in the virtual population allows the algorithm to behave, on one hand, as a multiple local search with a multi-start logic integrated within it. On the other hand, the compact units communicate among each other by means of a ring topology and propagation of information. More specifically, the most promising elite solutions and scale factor values of each compact unit are migrated to the neighbour unit so that the search of the global optimum is performed. In other words, while each single compact unit performs a local search by exploiting the direction suggested by each elite solution, the entire structure combines the achievement of each local search operation towards the direction of the global search. The proposed algorithm is characterized by a limited memory consumption and is memory-wise equivalent to a population-based algorithm with a small population. Numerical results show that the proposed approach outperforms other compact algorithms and various modern population-based structures.

Memetic Compact Differential Evolution for Cartesian Robot Control

This article deals with optimization problems to be solved in the absence of a full power computer device. The goal is to solve a complex optimization problem by using a control card related to portable devices, e.g. for the control of commercial robots. In order to handle this class of optimization problems, a novel Memetic Computing approach is presented. The proposed algorithm employs a Differential Evolution framework which instead of processing an actual population of candidate solutions, makes use of a statistical representation of the population which evolves over time. In addition, the framework uses a stochastic local search algorithm which attempts to enhance the performance of the elite. In this way, the memetic logic of performing the optimization by observing the decision space from complementary perspectives can be integrated within computational devices characterized by a limited memory. The proposed algorithm, namely Memetic compact Differential Evolution (McDE), has been tested and compared with other algorithms belonging to the same category for a real-world industrial application, i.e. the control system design of a cartesian robot for variable mass movements. For this real-world application, the proposed McDE displays high performance and has proven to considerably outperform other compact algorithms representing the current state-of-the-art in this sub-field of computational intelligence.