Conventional Evolutionary Algorithms Research Articles

Music Teaching Management and Music Culture Communication Environment Based on Ergonomics.

Music education has a relationship that is mutually restraining and interdependent. The necessity of spreading music culture offers music education social relevance and existential value in the history of music culture's growth, and the method of spreading music culture—music education—injects strength and life into the development of music culture. Music pedagogy is constrained by the evolution of music culture as a whole. In terms of music pedagogy, traditional culture and their peculiarities or qualities will have an impact on the creation of the curriculum content system, which is also the main topic of music teaching management. By using anthropometry, physiological and psychological measurement, etc. with the human body as the main body, ergonomics is the study of the reasonable coordination between the structure and function of the human body, psychology, mechanics, and music teaching methods in order to satisfy people's physical and mental activities and achieve the best use efficiency. The focus should be on comfort, great performance, safety, and health. In order to create a model for evaluating the quality of music instruction, a back propagation neural network (BPNN) is optimised in this paper using an adaptive mutation genetic algorithm. According to the research, our approach outperforms the BPNN model optimised using conventional evolutionary algorithm by 20.21%. The convergence pace is 80.11% faster than the convergence speed of a conventional genetic algorithm. The comparative result demonstrates that the BPNN teaching quality evaluation model with genetic algorithm optimization based on adaptive mutation can produce a more logical scientific evaluation result for the quality of music instruction.

A comparator-based constraint handling technique for evolutionary algorithms

Constraint handling is a key task for the successful optimization of design parameters in industrial design problems. This paper proposes a comparator-based constraint handling technique, called the More Less-Violations Method (MLVM), for solving real constrained optimization problems using evolutionary algorithms. The structure of the MLVM is simple and it can easily be integrated into conventional evolutionary algorithms. In the proposed method, constraint weights represent the level of importance of each constraint, enabling evolutionary compliance prioritization. Moreover, an acceptable region formed by the constraint tolerances allows trade-offs between objectives and constraints while preserving diverse solutions and improving optimization performance. These elements enable the appropriate design of industrial optimization problems. An application of this method to problems without constraint tolerances is also proposed. The JAXA/Mazda benchmark problem, developed on a real-world constrained design optimization dataset, is used to assess the performance of the MLVM. The results indicate that the MLVM realizes encouraging optimization performance.

Guest Editorial

Evolutionary Algorithms (EAs) are nature-inspired population-based search methods which work on Darwinian principles of natural selection. Due to their strong search capability and simplicity of implementation, EAs have been successfully applied to solve many complex optimization problems, which cannot be easily solved by traditional mathematical programming approaches, such as linear programming, quadratic programming, and convex optimization. Despite the great success enjoyed by EAs, it is worth noting that existing EA solvers usually conduct the search process from scratch, regardless of how similar the new problem encountered is to those already solved in the past. Therefore, conventional EAs do not learn from previous problems and the search capabilities of the EA solvers do not automatically grow with problem-solving experiences. However, in reality, since problems seldom exist in isolation, solving one problem may thus yield useful information for solving other related problems. In the literature, there is a growing interest in conducting research on evolutionary transfer optimization (ETO) in recent years: a paradigm that integrates EA solvers with knowledge learning and transfer across related domains to achieve better optimization efficiency and performance.

An MILP-Based Planning Model of a Photovoltaic/Diesel/Battery Stand-Alone Microgrid Considering the Reliability

For a stand-alone microgrid, the reliability criterion is often taken into account to guarantee the supply adequacy. This paper proposes a mixed integer linear programming (MILP) based model to determine the optimal sizing of a photovoltaic/diesel/battery stand-alone microgrid considering the reliability. Firstly, an economic-oriented microgrid planning model is developed. Subsequently, a rolling horizon based Sequential Monte Carlo method is developed to evaluate the system reliability. In particular, a multidimensional piecewise linearization method is proposed to approximate the nonlinear and implicit reliability evaluation function. Therefore the reliability criterion can be safely integrated into the microgrid planning model as an MILP problem which can be solved easily. The advantage of this method is that the costly reliability evaluation is calculated offline. Consequently, the time consumption of the online implement of the model becomes much less. The conventional techniques, however, integrate the reliability evaluation into the procedure. Thus a lot of iterations are needed to search for a solution that satisfies the reliability criterion. Finally, numerical experiments based on one microgrid test system are performed. The results indicate that larger sized DERs are deployed if a higher reliability criterion is required. Additionally, the proposed method is much faster than conventional evolutionary algorithms.

A novel evolutionary algorithm based on even difference grey model

Evolutionary algorithm is an important subdomain of the meta-heuristics as a kind of the most popular optimization technology. Inspired by the grey prediction theory, this paper proposes a novel evolution algorithm based on the even difference grey model. Unlike population-based meta-heuristics with employing mutation and crossover operators to generate trial populations, the proposed algorithm develops a reproduction operator by using the even difference grey model. The novel reproduction operator regards population series as a time series. It firstly transforms an unorder data selected from the population series to a series data with approximate exponential law by using a grey operator. Based on the series data generated, an exponential model is constructed by using the even difference grey model. Finally, the reproduction operator obtains a trail population according to the prediction results of the exponential model. The effectiveness and superiority of the proposed algorithm are demonstrated on CEC2005 benchmark functions and six real-world engineering design problems. Philosophically speaking, the proposed algorithm towards a brand-new point of view to achieve the optimization process by forecasting the evolutionary direction at the macroscopic level, while the conventional evolutionary algorithms manipulate the chromosomes to realize their optimization at the microscopic level. It could be hoped from the algorithm to open a door for other prediction theories to construct new meta-heuristics.

Gait Optimization of a Quadruped Robot Using Evolutionary Computation

Evolutionary Computation (EC) has strengths in terms of computation for gait optimization. However, conventional evolutionary algorithms use typical gait parameters such as step length and swing height, which limit the trajectory deformation for optimization of the foot trajectory. Furthermore, the quantitative index of fitness convergence is insufficient. In this paper, we perform gait optimization of a quadruped robot using foot placement perturbation based on EC. The proposed algorithm has an atypical solution search range, which is generated by independent manipulation of each placement that forms the foot trajectory. A convergence index is also introduced to prevent premature cessation of learning. The conventional algorithm and the proposed algorithm are applied to a quadruped robot; walking performances are then compared by gait simulation. Although the two algorithms exhibit similar computation rates, the proposed algorithm shows better fitness and a wider search range. The evolutionary tendency of the walking trajectory is analyzed using the optimized results, and the findings provide insight into reliable leg trajectory design.

A comprehensive survey on cultural algorithms

Cultural Algorithms (CAs) are evolutionary algorithms (EAs) inspired by the conceptual models of the human cultural evolution process. In contrast to the conventional EAs, which work only based on the population space, CAs employ an additional space, called belief space, to collect the information about the behaviour of individuals in the search space. Since the emergence of CAs, they have been successfully extended to solve a wide variety of problems in different branches of science and technology. In this paper, a comprehensive survey on the recent advances in CAs is presented. Literature survey reveals some interesting challenges and future research directions.

Paired Offspring Generation for Constrained Large-Scale Multiobjective Optimization

Constrained multiobjective optimization problems (CMOPs) widely exist in real-world applications, and they are challenging for conventional evolutionary algorithms (EAs) due to the existence of multiple constraints and objectives. When the number of objectives or decision variables is scaled up in CMOPs, the performance of EAs may degenerate dramatically and may fail to obtain any feasible solutions. To address this issue, we propose a paired offspring generation-based multiobjective EA for constrained large-scale optimization. The general idea is to emphasize the role of offspring generation in reproducing some promising feasible or useful infeasible offspring solutions. We first adopt a small set of reference vectors for constructing several subpopulations with a fixed number of neighborhood solutions. Then, a pairing strategy is adopted to determine some pairwise parent solutions for offspring generation. Consequently, the pairwise parent solutions, which could be infeasible, may guide the generation of well-converged solutions to cross the infeasible region(s) effectively. The proposed algorithm is evaluated on CMOPs with up to 1000 decision variables and ten objectives. Moreover, each component in the proposed algorithm is examined in terms of its effect on the overall algorithmic performance. Experimental results on a variety of existing and our tailored test problems demonstrate the effectiveness of the proposed algorithm in constrained large-scale multiobjective optimization.

Development of an efficient global optimization method based on adaptive infilling for structure optimization

For problems with expensive black-box functions, the surrogate-based optimization (SBO) is more efficient than the conventional evolutionary algorithms in searching for the global optimum. However, the SBO converges much slower and shows imperfection in local exploitation, along with the increase of the scale of the design space, the number of the design variables, and the nonlinearity of the problems. This paper proposes an efficient global optimization method, which integrates an adaptive infilling by fuzzy clustering algorithm into an SBO process based on Kriging model. In each refinement cycle, a Kriging model is first built using samples in the current design space; then a fuzzy clustering algorithm is adopted to partition the design space into several subspaces considering inner features of the samples. Thus, new infilling samples are selected within each subspace by maximizing the expected improvement of the objective function and minimizing the surrogate prediction. Thereafter, the design space is updated by merging those subspaces, resulting in a diminishing design space during refinement. Furthermore, the parameters for the adaptive infilling procedure are studied to recommend reasonable settings for running optimizations. The proposed method is finally validated and assessed by eight analytical tests with bound constraints, and then employed in a beam optimization problem and a rocket interstage optimization problem under nonlinear constraints. The results indicate that the adaptive infilling behaves quite well in space exploration due to sampling in clustered subspaces, and possesses good performance in local exploitation as well because of space reduction.

Application of particle swarm optimization to water management: an introduction and overview.

Particle swarm optimization (PSO) is a stochastic population-based optimization algorithm inspired by the interactions of individuals in a social world. This algorithm is widely applied in different fields of water resources problems. This paper presents a comprehensive overview of the basic PSO algorithm search strategy and PSO's applications and performance analysis in water resources engineering optimization problems. Our literature review revealed 22 different varieties of the PSO algorithm. The characteristics of each PSO variety together with their applications in different fields of water resources engineering (e.g., reservoir operation, rainfall-runoff modeling, water quality modeling, and groundwater modeling) are highlighted. The performances of different PSO variants were compared with other evolutionary algorithms (EAs) and mathematical optimization methods. The review evaluates the capability and comparative performance of PSO variants over conventional EAs (e.g., simulated annealing, differential evolution, genetic algorithm, and shark algorithm) and mathematical methods (e.g., support vector machine and differential dynamic programming) in terms of proper convergence to optimal Pareto fronts, faster convergence rate, and diversity of computed solutions.

Analysis of semi-asynchronous multi-objective evolutionary algorithm with different asynchronies

This paper proposes a novel master–slave parallel evolutionary algorithm (EA) approach with different asynchrony and provides its detailed analyses on multi-objective optimization problems. We express the proposed EA with different asynchrony as a semi-asynchronous EA. A semi-asynchronous EA generates new solutions whenever evaluations of the predefined number of solutions complete, unlike a conventional synchronous EA waits for evaluations of all solutions to generate the next population. To establish a semi-asynchronous EA, this paper introduces an asynchrony parameter that is used to decide how many solutions are waited to generate new solutions. We conduct an experiment to verify the effectiveness of the proposed semi-asynchronous EA on benchmark problems with the several variances of the evaluation time. In the experiment, we apply a semi-asynchronous EA to NSGA-II and NSGA-III, which are well-known multi-objective EAs. The semi-asynchronous NSGA-IIs and the semi-asynchronous NSGA-IIIs with different asynchronies are compared on multi-objective optimization benchmark problems. The experimental result reveals that semi-asynchronous approaches with an appropriate asynchrony have possibility to outperform the asynchronous and the synchronous ones. Additionally, detailed analysis reveals that an appropriate asynchrony may vary not only depends on a target problem but also depends on the degree of the evolution process.

Generalized Multitasking for Evolutionary Optimization of Expensive Problems

Conventional evolutionary algorithms (EAs) are not well suited for solving expensive optimization problems due to the fact that they often require a large number of fitness evaluations to obtain acceptable solutions. To alleviate the difficulty, this paper presents a multitasking evolutionary optimization framework for solving computationally expensive problems. In the framework, knowledge is transferred from a number of computationally cheap optimization problems to help the solution of the expensive problem on the basis of the recently proposed multifactorial EA (MFEA), leading to a faster convergence of the expensive problem. However, existing MFEAs do not work well in solving multitasking problems whose optimums do not lie in the same location or when the dimensions of the decision space are not the same. To address the above issues, the existing MFEA is generalized by proposing two strategies, one for decision variable translation and the other for decision variable shuffling, to facilitate knowledge transfer between optimization problems having different locations of the optimums and different numbers of decision variables. To assess the effectiveness of the generalized MFEA (G-MFEA), empirical studies have been conducted on eight multitasking instances and eight test problems for expensive optimization. The experimental results demonstrate that the proposed G-MFEA works more efficiently for multitasking optimization and successfully accelerates the convergence of expensive optimization problems compared to single-task optimization.

Evolutionary algorithms application for improving the tire rolling resistance based on Wismer–Luth model

Soil and tire interaction is a complex process that involves the exchange of variable stresses along the contact area of soil and tire. Despite this complexity, the description of this process in the form of mathematical models has long been of interest to the researchers. The same complexity has led the wheels and soil interaction patterns to be constantly evolving and optimizing. This evolution has coincided with the scientific progress of mathematics, modeling and computer until today. Nowadays, optimizing and predicting a model based on input variables using machine learning techniques and conventional evolutionary algorithms play an important role in predicting the relationships between input and output. These methods can be far better than the conventional statistical techniques. The modeling and prediction of wheel rolling resistance on the soil have many parameters. Using new techniques such as genetic, BAT and PSO algorithms to optimize them seems to be suitable approaches. The aim of this research is to investigate and optimize the parameters of the Wismer–Luth model using the evolutionary algorithms. To improve the model, the variables of multi-pass, forward velocity and depth of the cone index, are also incorporated to the Wismer–Luth model, and the corresponding parameters are optimized with the BAT algorithm. Analysis of experimental data showed that the correlation of the output of the proposed model with the experimental data is 0.87 where it is 0.77 for the Wismer–Luth model. Furthermore, experimental results in this study showed that there is a significant relationship between rolling resistance and multi-pass effect, neglected in most models.

An improved decomposition based multi-objective evolutionary algorithm for the operation management of a renewable micro-grid

This study presents a multiobjective energy management to optimize the renewable microgrid operation while satisfying a demand response and various operation constraints. With regard to energy cost minimization, pollutant emission reduction for better utilization of the renewable energy resources, such as wind and solar, as a competitive objective is proposed. Moreover, for maximizing the renewable microgrid operators, demand response benefits satisfying the load demand constraints amongst other operation constraints are incorporated into the operations of the renewable microgrid. The overall problem is formulated as a mixed-integer nonlinear constraint multiobjective optimization problem with different equality and inequality constraints. In this paper, an improved decomposition based multiobjective evolutionary algorithm is presented for optimal operation of the renewable microgrid with renewable energy sources and various devices such as diesel generators, micro-turbines, fuel cells, and battery energy storage. To improve the optimization process, differential evolution (DE) and the niche guided mating selection strategy are incorporated. Meanwhile, decomposition-based multiobjective evolutionary algorithm-DE is extended to tackle the constrained optimization problem. Finally, the proposed algorithm is applied in a renewable microgrid, and its superior performance is compared with the conventional evolutionary algorithms such as the multiobjective genetic algorithm and the original decomposition based multiobjective evolutionary algorithm.

Adaptive Knowledge-Driven Optimization for Architecting a Distributed Satellite System

As distributed satellite systems gain interest, there is a growing need for design tools that can identify system architectures with good trades in multiple metrics. Evolutionary algorithms have shown promise as effective design-support tools but are computationally inefficient because they require evaluating many architectures before identifying ones with good tradeoffs. Knowledge-driven optimization aims to improve evolutionary algorithms by including a data-mining algorithm that learns design features common in the best architectures found during the optimization. These design features can guide the remainder of the optimization but should be used carefully to prevent misleading the search. This work presents a new knowledge-driven optimization framework, knowledge-driven optimization (KDO)/adaptive operator selection (AOS), that monitors how well the design features guide the search toward good architectures and focuses on using the best features. If none are beneficial to the optimization, KDO/AOS adapts its search strategy to explore regions of the trade space not captured by the design features. KDO/AOS is demonstrated on an architecture design problem for a distributed satellite system, and its adaptive search strategy enables it to discover high-quality architectures with over 800 fewer evaluations when compared with a conventional evolutionary algorithm. KDO/AOS is also more robust than other knowledge-driven optimization algorithms to inaccuracies from data mining.

Numerical Optimization and Noise Analysis of High-Tip-Speed Wind Turbine

AbstractWith lower turbulence and less rigorous restrictions on noise levels, offshore wind farms provide favourable conditions for the development of high-tip-speed wind turbines. In this study, the multi-objective optimization is presented for a 5MW wind turbine design and the effects of high tip speed on power output, cost and noise are analysed. In order to improve the convergence and efficiency of optimization, a novel type of gradient-based multi-objective evolutionary algorithm is proposed based on uniform decomposition and differential evolution. Optimization examples of the wind turbines indicate that the new algorithm can obtain uniformly distributed optimal solutions and this algorithm outperforms the conventional evolutionary algorithms in convergence and optimization efficiency. For the 5MW wind turbines designed, increasing the tip speed can greatly reduce the cost of energy (COE). When the tip speed increases from 80m/s to 100m/s, under the same annual energy production, the COE decreases by 3.2% in a class I wind farm and by 5.1% in a class III one, respectively, while the sound pressure level increases by a maximum of 4.4dB with the class III wind farm case.

Competitive co-evolution of multi-layer perceptron classifiers

This paper analyses the competitive approach to the co-evolutionary training of multi-layer perceptron classifiers. Two algorithms were tested: the first opposes a population of classifiers to a population of training patterns; the second pits a population of classifiers against a population of subsets of training patterns. The classifiers are regarded as predators that need to ‘capture’ (correctly categorise) the prey (training patterns). Success for the predators is measured on their ability to capture prey. Success for the prey is measured on their ability to escape predation (be misclassified). The aim of the procedure is to create an evolutionary tug-of-war between the best classifiers and the most difficult data samples, increasing the efficiency and accuracy of the learning process. The two co-evolutionary algorithms were tested on a number of well-known benchmarks and on several artificial data sets modelling different kinds of common classification problems such as overlapping data categories, noisy training inputs, and unbalanced data classes. The performance of the co-evolutionary methods was compared with that of two traditional training techniques: the standard backpropagation rule and a conventional evolutionary algorithm. The co-evolutionary procedures achieved top accuracy in all classification problems. They particularly excelled on data sets containing noisy training inputs, where they outperformed the backpropagation rule, and on tasks involving unbalanced data classes, where they outperformed both backpropagation and the conventional evolutionary algorithm. Compared to the standard evolutionary algorithm, the co-evolutionary procedures were able to obtain similar or superior learning accuracies, whilst needing considerably less presentations of the training patterns. This economy in the use of training patterns translated into significant savings in computational overheads and algorithms running time.

Inclined planes system optimization algorithm for IIR system identification

In this paper the digital infinite impulse response (IIR) filter design is modeled as an optimization problem. A new design method based on inclined planes system optimization (IPO) is introduced for the IIR system identification. IPO is a heuristic technique based on the dynamics of sliding motion along a frictionless inclined surface that has been demonstrated the reliable performance in solving of engineering complex problems. The effectiveness of the proposed method is verified in presence of the additive noise. In this work, both actual and reduced order identification of few benchmarked IIR plants is carried out in the simulations. Newton’s Mechanics-based, swarm intelligence based and conventional evolutionary algorithms are used to model the same examples and simulation results are evaluated. The final results clearly demonstrate the good performance and premier identification of the proposed method along with well-tuned other algorithms.

Target shape design optimization by evolving B-splines with cooperative coevolution

With high reputation in handling non-linear and multi-model problems with little prior knowledge, evolutionary algorithms (EAs) have successfully been applied to design optimization problems as robust optimizers. Since real-world design optimization is often computationally expensive, target shape design optimization problems (TSDOPs) have been frequently used as efficient miniature model to check algorithmic performance for general shape design. There are at least three important issues in developing EAs for TSDOPs, i.e., design representation, fitness evaluation and evolution paradigm. Existing work has mainly focused on the first two issues, in which (1) an adaptive encoding scheme with B-spline has been proposed as a representation, and (2) a symmetric Hausdorff distance based metric has been used as a fitness function. But for the third issue, off-the-shelf EAs were used directly to evolve B-spline control points and/or knot vector. In this paper, we first demonstrate why it is unreasonable to evolve the control points and knot vector simultaneously. And then a new coevolutionary paradigm is proposed to evolve the control points and knot vector of B-spline separately in a cooperative manner. In the new paradigm, an initial population is generated for both the control points, and the knot vector. The two populations are evolved mostly separately in a round-robin fashion, with only cooperation at the fitness evaluation phase. The new paradigm has at least two significant advantages over conventional EAs. Firstly, it provides a platform to evolve both the control points and knot vector reasonably. Secondly, it reduces the difficulty of TSDOPs by decomposing the objective vector into two smaller subcomponents (i.e., control points and knot vector). To evaluate the efficacy of the proposed coevolutionary paradigm, an algorithm named CMA-ES-CC was formulated. Experimental studies were conducted based on two target shapes. The comparison with six other EAs suggests that the proposed cooperative coevolution paradigm is very effective for TSDOPs.

Improving evolutionary algorithm performance for integer type multi-objective building system design optimization

Building system design optimization is becoming popular for design decision making. State-of-the-art technique that couples evolutionary algorithms with a building simulation engine, which is time consuming and often cannot reach the “true” optimal solutions. Studies addressing these issues focus on implementing strategies such as fine tuning optimization algorithm's parameters, hybrid evolutionary algorithms with a local search algorithm or optimizing meta-models. Unlike the previous studies, this paper proposes two improvement strategies for building system design optimization. The two strategies, adaptive operators approach and adaptive meta-model approach, modify the behaviors of conventional evolutionary algorithms to improve the optimization convergency and speed performance. To demonstrate the effectiveness of these two strategies compared to conventional algorithms, a case study was conducted. The case study observed high convergency performance from both strategies with 30% and 60% time savings respectively. Furthermore, this study examines the performance comparison in respect to convergency, diversity preservation and speed between these two strategies.