Constrained Evolution Research Articles

Evolutionary dynamic constrained optimization: Test suite construction and algorithm comparisons

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.

A multi-objective differential evolutionary algorithm for constrained multi-objective optimization problems with low feasible ratio

Most current evolutionary multi-objective optimization (EMO) algorithms perform well on multi-objective optimization problems without constraints, but they encounter difficulties in their ability for constrained multi-objective optimization problems (CMOPs) with low feasible ratio. To tackle this problem, this paper proposes a multi-objective differential evolutionary algorithm named MODE-SaE based on an improved epsilon constraint-handling method. Firstly, MODE-SaE self-adaptively adjusts the epsilon level in line with the maximum and minimum constraint violation values of infeasible individuals. It can prevent epsilon level setting from being unreasonable. Then, the feasible solutions are saved to the external archive and take part in the population evolution by a co-evolution strategy. Finally, MODE-SaE switches the global search and local search by self-switching parameters of search engine to balance the convergence and distribution. With the aim of evaluating the performance of MODE-SaE, a real-world problem with low feasible ratio in decision space and fourteen bench-mark test problems, are used to test MODE-SaE and five other state-of-the-art constrained multi-objective evolution algorithms. The experimental results fully demonstrate the superiority of MODE-SaE on all mentioned test problems, which indicates the effectiveness of the proposed algorithm for CMOPs which have low feasible ratio in search space.

Joint cooperative spectrum sensing and primary user emulation attack detection in cognitive radio networks using fuzzy conditional entropy maximization

AbstractCooperative spectrum sensing (CSS) in cognitive radio system shows improved performance on spectrum sensing but, at the same time, faces a common threat called primary user emulation attack (PUEA) that impersonates the primary user (PU). This work explores joint PU and PUEA detection on CSS as a multiclass hypothesis using fuzzy conditional entropy maximization. To meet the goal, a multithreshold approach is suggested using a constrained differential evolution algorithm search to determine an optimal set of fuzzy function parameters, which maximizes the conditional entropy on the energy values. The proposed work also investigates the performance of CSS in presence of a group of malicious users (as PUEA), which generates signals and directly sends the same to the fusion center. Numerical results through simulation illustrate the efficacy of the proposed scheme in terms of both reliable PU detection in presence or absence of PUEA/malicious user and separate detection of falsifying PU and energy consumption in CSS. Simulation results show that the proposed scheme offers ∼17.54% and ∼39.39% higher detection probability of PU over the existing works in presence of PUEA.

Protein docking using constrained self-adaptive differential evolution algorithm

The objective of protein docking is to achieve a relative orientation and an optimized conformation between two proteins that results in a stable structure with the minimized potential energy. Constrained self-adaptive differential evolution (Cons_SaDE) algorithm is used to find the minimum energy conformation using proposed constraints such as boundary surface complementary interactions, non-bonded inter-atomic allowed distances and finding of interaction and non-interaction sites. With these constraints, Cons_SaDE is efficient enough to explore the promising solutions by gradually self-adapting the strategies and parameters learned from their previous experiences. Modified sampling scheme called rotate only representation is used to represent a docking conformation. GROMOS53A6 force field is used to find the potential energy. To test the performance of this algorithm, few bound and unbound complexes from Protein Data Bank (PDB) and few easy, medium and difficult complexes from Zlab Benchmark 4.0 are used. Buried surface area, root-mean-square deviation (RMSD) and correlation coefficient are some of the metrics applied to evaluate the best docked conformations. RMSD values of the best docked conformations obtained from five popular docking Web servers are compared with Cons_SaDE results, and nonparametric statistical tests for multiple comparisons with control method are implemented to show the performance of this algorithm. Cons_SaDE has produced good-quality solutions for the most of the data sets considered.

High-Dimensional Robust Multi-Objective Optimization for Order Scheduling: A Decision Variable Classification Approach

This paper tackles the high-dimensional robust order scheduling problem. A multi-objective evolutionary algorithm called constrained nondominated sorting differential evolution based on decision variable classification is developed to search for robust order schedules. The decision variables are classified into highly and weakly robustness-related variables according to their contributions to the robustness of candidate solutions. The experimental results reveal that the performance of robust evolutionary optimization can be greatly improved via analyzing the properties of decision variables and then decomposing the high-dimensional robust optimization problem. It is also unveiled that the order scheduling is greatly affected by the uncertain daily production quantities. The robust order schedules are able to provide more information on earliness/tardiness of the orders, which enhances the flexibility of the production.

Health Status Prediction Based on Belief Rule Base for High-Speed Train Running Gear System

The running gear is a vital component of a high-speed train to ensure operation safety. Accurately predicting the future health status of the running gear is significant to keep the reliability and safety of trains. It is difficult to predict the future health status based on the analytical model of the running gear system because of its complexity and coupling. Moreover, the fault data are a minor part of tremendous data in the running and monitoring process of a high-speed train, which obstructs accurately predicting the health status based on a data-driven method. To solve the above problems, this paper proposes a health status prediction method based on the belief rule base (BRB) for the running gear system. First, a failure mechanism is analyzed to confirm the fault characteristics, which can represent the health status of the running gear system. Second, in order to avoid the limitations of a single sensor acquisition, such as a lack of comprehensiveness and robustness, singular value decomposition is used to achieve multisensory information fusion. The fused features are used as the input to the health status prediction model. Data fusion is a way to improve the precision of the health status prediction in the model input. Then, this model based on the BRB is established using the fault data and expert knowledge. During the process of prediction, the subjectivity of experts makes the initial BRB imprecise, so a projection constrained covariance matrix adaptive evolution strategy algorithm is needed to optimize the initial parameters and improve the accuracy of the proposed model. Finally, a case study for the running gear system is carried out to verify the effectiveness and accuracy of the proposed model. The results show that the proposed model can help to accurately predict the health status of the running gear system.

Genomic Signals of Adaptation towards Mutualism and Sociality in Two Ambrosia Beetle Complexes.

Mutualistic symbiosis and eusociality have developed through gradual evolutionary processes at different times in specific lineages. Like some species of termites and ants, ambrosia beetles have independently evolved a mutualistic nutritional symbiosis with fungi, which has been associated with the evolution of complex social behaviors in some members of this group. We sequenced the transcriptomes of two ambrosia complexes (Euwallacea sp. near fornicatus–Fusarium euwallaceae and Xyleborus glabratus–Raffaelea lauricola) to find evolutionary signatures associated with mutualism and behavior evolution. We identified signatures of positive selection in genes related to nutrient homeostasis; regulation of gene expression; development and function of the nervous system, which may be involved in diet specialization; behavioral changes; and social evolution in this lineage. Finally, we found convergent changes in evolutionary rates of proteins across lineages with phylogenetically independent origins of sociality and mutualism, suggesting a constrained evolution of conserved genes in social species, and an evolutionary rate acceleration related to changes in selective pressures in mutualistic lineages.

Classically and Asteroseismically Constrained 1D Stellar Evolution Models of α Centauri A and B Using Empirical Mixing Length Calibrations

Abstract The bright, nearby binary α Centauri provides an excellent laboratory for testing stellar evolution models, because it is one of the few stellar systems for which we have high-precision classical (mass, radius, luminosity) and asteroseismic (p-mode) observations. Stellar models are created and fit to the classical and seismic observations of both stars by allowing for the free variation of the convective mixing length parameter α MLT. This system is modeled using five different sets of assumptions about the physics governing the stellar models. There are 31 pairs of tracks (out of ∼150,000 generated) that fit the classical, binary, and seismic observational constraints of the system within 3σ. Models with each tested choice of input physics are found to be viable, but the optimal mixing lengths for α Cen A and α Cen B remain the same regardless of the physical prescription. The optimal mixing lengths are α MLT,A/α ⊙ = 0.932 and α MLT,B/α ⊙ = 1.095. That α Cen A and α Cen B require subsolar and supersolar mixing lengths, respectively, to fit the observations is a trend consistent with recent findings, such as those of Kervella et al., Joyce & Chaboyer, and Viani et al. The optimal models find an age for α Centauri of 5.3 ± 0.3 Gyr.

A class of evolution variational inequalities with nonconvex constraints

ABSTRACTWe study a nonconvex constrained evolution problem for a set being the union of a finite number of convex sets. The problem generalizes the classical parabolic variational inequality of the second kind and contains an operator of L--type. The existence result for this problem is proved using a variational-hemivariational inequality approach, a surjectivity theorem for multivalued pseudomonotone operators in a reflexive Banach space and a penalization method in which a small parameter does not have to tend to zero.

Cooperative Differential Evolution Framework for Constrained Multiobjective Optimization.

This paper presents a cooperative differential evolution framework (CCMODE) for constrained multiobjective optimization, and two instantiations of the CCMODE framework are implemented. The proposed framework has (M+1) populations, including M subpopulations for constrained single-objective optimization and an archive population for constrained M -objective optimization. Each subpopulation performs its own constrained single-objective differential evolution to optimize the assigned constrained single-objective optimization problem. For the archive population, the constraint handling techniques (CHTs) are modified for constrained multiobjective optimization. The proposed framework takes the advantage of existing effective constrained single-objective optimization algorithms, and extends them to deal with constrained multiobjective optimization problems. In two instantiations, two CHTs are implemented in CCMODE framework, respectively. Experiment results on several sets of benchmark problems with two, three, and many objectives show that the proposed algorithm is better than existing state-of-the-art constrained multiobjective evolutionary algorithms. The effectiveness of the subpopulations is also discussed.

Links between Anatomy, Systems biology, Epigenetics, etho‐eco‐morphological mismatches, evolutionary trends, and organism behavior: a unifying view of anatomical evolution

It is a puzzling paradox that eco‐morphological mismatches occur so frequently in an evolutionary process that often leads to macroevolutionary trends and in which organisms are said to be ‘designed’ for the habitats they inhabit. Here I present a new framework ‐ Organic Nonoptimal Constrained Evolution (ONCE) ‐ to address this paradox, and to explain why organisms look as they look: organisms themselves, and in particular their behavior, are the major active players of evolution. That is, within this framework, internal factors can both decrease and increase plasticity/hidden variation and therefore, together with epigenetic factors influenced by the external environment, can allows organisms to shift their behavior, for instance as a response to environmental changes. Importantly, due to behavioral persistence related to behavioral/ecological inheritance, organisms as diverse as bacteria, plants and animals help to construct their own niches, thus being crucial to direct evolution. Darwinian natural (external) selection then comes into play as a secondary ‐ but still crucial ‐ player. That is, due to organismal behavioral persistence, the random mutations/epigenetic factors that happen to be advantageous within the niches constructed by the organisms will be selected, further directing evolution and increasing the match between behavior, phenotype, and environment. This process can extend for long periods of time, leading to macroevolutionary morphological trends and further increasing this match, potentially resulting in successful phenotypic overspecialization. However, behavioral persistence, loss of plasticity due to natural selection, genetic drift, overspecialization, and internal constraints can often make it difficult for the organisms to respond behaviorally and/or anatomically to new environmental changes, resulting in potential mismatches between behavior, ecology and form, and eventually in extinction. This new framework about anatomical evolution therefore bridges the gap between ideas that have been long considered to be opposing views of biology since Aristotle, including internalism and externalism, Cuvier's vs. Geoffroy's take on form and function, Darwinism and Lamarkism, the ideas of Baldwin, Waddington and Goldschmidt, and current Evo‐Devo ideas such as a physicalist framework, niche construction and epigenetic plasticity. This talk therefore covers ‐ and aims to link ‐ areas as diverse as evolutionary developmental biology, paleontology, evolutionary biology, comparative anatomy, ecomorphology, functional anatomy, ecology, ethology, biological anthropology, evolutionary medicine and history and philosophy of biology.Support or Funding InformationNAThis abstract is from the Experimental Biology 2018 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.

Applying Unique Molecular Identifiers in Next Generation Sequencing Reveals a Constrained Viral Quasispecies Evolution under Cross-Reactive Antibody Pressure Targeting Long Alpha Helix of Hemagglutinin.

To overcome yearly efforts and costs for the production of seasonal influenza vaccines, new approaches for the induction of broadly protective and long-lasting immune responses have been developed in the past decade. To warrant safety and efficacy of the emerging crossreactive vaccine candidates, it is critical to understand the evolution of influenza viruses in response to these new immune pressures. Here we applied unique molecular identifiers in next generation sequencing to analyze the evolution of influenza quasispecies under in vivo antibody pressure targeting the hemagglutinin (HA) long alpha helix (LAH). Our vaccine targeting LAH of hemagglutinin elicited significant seroconversion and protection against homologous and heterologous influenza virus strains in mice. The vaccine not only significantly reduced lung viral titers, but also induced a well-known bottleneck effect by decreasing virus diversity. In contrast to the classical bottleneck effect, here we showed a significant increase in the frequency of viruses with amino acid sequences identical to that of vaccine targeting LAH domain. No escape mutant emerged after vaccination. These results not only support the potential of a universal influenza vaccine targeting the conserved LAH domains, but also clearly demonstrate that the well-established bottleneck effect on viral quasispecies evolution does not necessarily generate escape mutants.

Structural Reliability Assessment Based on the Improved Constrained Differential Evolution Algorithm

In this work, the reliability analysis is employed to take into account the uncertainties in a structure. Reliability analysis is a tool to compute the probability of failure corresponding to a given failure mode. In this study, one of the most commonly used reliability analysis method namely first order reliability method is used to calculate the probability of failure. Since finding the most probable point (MPP) or design point is a constrained optimization problem, in contrast to all the previous studies based on the penalty function method or the preference of the feasible solutions technique, in this study one of the latest versions of the differential evolution metaheuristic algorithm named improved (μ+λ)-constrained differential evolution (ICDE) based on the multi-objective constraint-handling technique is utilized. The ICDE is very easy to implement because there is no need to the time-consuming task of fine tuning of the penalty parameters. Several test problems are used to verify the accuracy and efficiency of the ICDE. The statistical comparisons revealed that the performance of ICDE is better than or comparable with the other considered methods. Also, it shows acceptable convergence rate in the process of finding the design point. According to the results and easier implementation of ICDE, it can be expected that the proposed method would become a robust alternative to the penalty function based methods for the reliability assessment problems in the future works.

Differential Rates of Male Genital Evolution in Sibling Species of Drosophila

Fil: Stefanini, Manuel Ignacio. Consejo Nacional de Investigaciones Cientificas y Tecnicas. Oficina de Coordinacion Administrativa Ciudad Universitaria. Instituto de Ecologia, Genetica y Evolucion de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Ecologia, Genetica y Evolucion de Buenos Aires; Argentina. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ecologia, Genetica y Evolucion; Argentina

Optimal trajectory planning of free-floating space manipulator using differential evolution algorithm

The existence of the path dependent dynamic singularities limits the volume of available workspace of free-floating space robot and induces enormous joint velocities when such singularities are met. In order to overcome this demerit, this paper presents an optimal joint trajectory planning method using forward kinematics equations of free-floating space robot, while joint motion laws are delineated with application of the concept of reaction null-space. Bézier curve, in conjunction with the null-space column vectors, are applied to describe the joint trajectories. Considering the forward kinematics equations of the free-floating space robot, the trajectory planning issue is consequently transferred to an optimization issue while the control points to construct the Bézier curve are the design variables. A constrained differential evolution (DE) scheme with premature handling strategy is implemented to find the optimal solution of the design variables while specific objectives and imposed constraints are satisfied. Differ from traditional methods, we synthesize null-space and specialized curve to provide a novel viewpoint for trajectory planning of free-floating space robot. Simulation results are presented for trajectory planning of 7 degree-of-freedom (DOF) kinematically redundant manipulator mounted on a free-floating spacecraft and demonstrate the feasibility and effectiveness of the proposed method.

Do Muscles Constrain Skull Shape Evolution in Strepsirrhines?

Despite great interest and decades of research, the musculoskeletal relationships of the masticatory system in primates are still not fully understood. However, without a clear understanding of the interplay between muscles and bones it remains difficult to understand the functional significance of morphological traits of the skeleton. Here, we aim to study the impacts of the masticatory muscles on the shape of the cranium and the mandible as well as their co-variation in strepsirrhine primates. To do so, we use 3D geometric morphometric approaches to assess the shape of each bone of the skull of 20 species for which muscle data are available in the literature. Impacts of the masticatory muscles on the skull shape were assessed using non-phylogenetic regressions and phylogenetic regressions whereas co-variations were assessed using two-blocks partial least square (2B-PLS) and phylogenetic 2B-PLS. Our results show that there is a phylogenetic signal for skull shape and masticatory muscles. They also show that there is a significant impact of the masticatory muscles on cranial shape but not as much as on the mandible. The co-variations are also stronger between the masticatory muscles and cranial shape even when taking into account phylogeny. Interestingly, the results of co-variation between the masticatory muscles and mandibular shape show a more complex pattern in two different directions to get strong muscles associated with mandibular shape: a folivore way (with the bamboo lemurs and sifakas) and a hard-object eater one (with the aye-aye). Anat Rec, 301:291-310, 2018. © 2018 Wiley Periodicals, Inc.

The Krasnosel'skii formula for parabolic differential inclusions with state constraints

We consider a constrained semilinear evolution inclusion of parabolic type involving an $m$-dissipative linear operator and a source term of multivalued type in a Banach space and topological properties of the solution map. We establish the $R_δ$-description of the set of solutions surviving in the constraining area and show a relation between the fixed point index of the Krasnosel'skii-Poincaré operator of translation along trajectories associated with the problem and the appropriately defined constrained degree of the right-hand side in the equation. This provides topological tools appropriate to obtain results on the existence of periodic solutions to studied differential problems.

Constrained evolution algorithm based on adaptive differential evolution

Constrained evolution algorithm based on adaptive differential evolution

Constrained evolution algorithm based on adaptive differential evolution

Solving constrained optimisation is widely used in science and engineering, but the slow convergence speed and premature are the biggest problems researchers face. Research on a constrained evolution algorithm (CO-JADE) based on adaptive differential evolution (JADE) for solving the constrained optimisation problems is proposed in this paper. According to features of the Gaussian distribution, the Cauchy distribution and the mutation factor, we exploited the crossover probability of each individual to improve the search strategy. Aimed to effectively evaluate the relationship between the value of the objective function and the degree of constraint violation, the paper used an improved adaptive tradeoff model to evaluate the individuals of the population. This tradeoff model used different treatment scheme for different stages of the population and implemented on night standard test functions. The experimental shows that the CO-JADE has better accuracy and stability than the COEA/ODE and the HCOEA.

Constrained vertebrate evolution by pleiotropic genes.

Despite morphological diversification of chordates over 550 million years of evolution, their shared basic anatomical pattern (or 'bodyplan') remains conserved by unknown mechanisms. The developmental hourglass model attributes this to phylum-wide conserved, constrained organogenesis stages that pattern the bodyplan (the phylotype hypothesis); however, there has been no quantitative testing of this idea with a phylum-wide comparison of species. Here, based on data from early-to-late embryonic transcriptomes collected from eight chordates, we suggest that the phylotype hypothesis would be better applied to vertebrates than chordates. Furthermore, we found that vertebrates' conserved mid-embryonic developmental programmes are intensively recruited to other developmental processes, and the degree of the recruitment positively correlates with their evolutionary conservation and essentiality for normal development. Thus, we propose that the intensively recruited genetic system during vertebrates' organogenesis period imposed constraints on its diversification through pleiotropic constraints, which ultimately led to the common anatomical pattern observed in vertebrates.