Stochastic Evolutionary Algorithm Research Articles

Application of stochastic ranking based evolutionary strategy in environmental management of microgrids

The microgrid environmental governance problem is a single objective function, and the biogeography algorithm traditionally used for the calculation has significant shortcomings. The algorithm has a limited search range at the beginning of the iteration and tends to fall into a local optimum at the end. In addition, it is slow to converge and poor in finding the best solution when solving microgrid scheduling optimization problems. In this paper, we adopt a typical microgrid environmental management framework model and use a stochastic ranking evolutionary strategy algorithm with high performance. The objective function is to minimize the ecological management cost and optimize the rational dispatch of electricity to gas and energy storage devices. The case study proves that the stochastic ranking evolutionary strategy algorithm is more efficient and converges faster. This algorithm can effectively solve the energy dispatching optimization problem in microgrids.

An iterative cyclic tri-strategy hybrid stochastic fractal with adaptive differential algorithm for global numerical optimization

Many real-life problems can be formulated as numerical optimization problems. Such problems pose a challenge for researchers when designing efficient techniques that are capable of finding the desired solution without suffering from premature convergence. This paper proposes a novel evolutionary algorithm that blends the exploitative and explorative merits of two main evolutionary algorithms, namely the Stochastic Fractal Search (SFS) and a Differential Evolution (DE) variant. This amalgam has an effective interaction and cooperation of an ensemble of diverse strategies to derive a single framework called Iterative Cyclic Tri-strategy with adaptive Differential Stochastic Fractal Evolutionary Algorithm (Ic3-aDSF-EA). The component algorithms cooperate and compete to enhance the quality of the generated solutions and complement each other. The iterative cycles in the proposed algorithm consist of three consecutive phases. The main idea behind the cyclic nature of Ic3-aDSF-EA is to gradually emphasize the work of the best-performing algorithm without ignoring the effects of the other inferior algorithm during the search process. The cooperation of component algorithms takes place at the end of each cycle for information sharing and the quality of solutions for the next cycle. The algorithm's performance is evaluated on 43 problems from three different benchmark suites. The paper also investigates the application to a set of real-life problems. The overall results show that the proposed Ic3-aDSF-EA has a propitious performance and a reliable scalability behavior compared to other state-of-the-art algorithms.

A sequential optimal Latin hypercube design method using an efficient recursive permutation evolution algorithm

Latin hypercube design (LHD) is one of the most frequently used sampling methods. However, most LHDs generate data samples in a manner that hinders computational efficiency and space-filling performance when high dimensions and large samples are involved. Therefore, a sequential recursive evolution Latin hypercube design (RELHD) is proposed in this article, which adopts a permutation inheritance algorithm to update and optimize the LHD. A recursive split algorithm is also proposed and used to enhance the computational efficiency by dividing the sample set into smaller subsets. Numerical experiments demonstrate that the space-filling quality of the RELHD compares well with the enhanced stochastic evolutionary algorithm (ESE) in complex problems with large samples and high dimensions, with RELHD having a significantly higher computational efficiency than ESE. Finally, the sequential approach of RELHD proves to be a more efficient strategy when dealing with sampling-based analysis problems.

Application of Temperature and Process Duration as a Method for Predicting the Mechanical Properties of Thermally Modified Timber

This study aims to investigate the influence of thermal modification (TM) on the physical and mechanical properties of wood. For this purpose, the experimental part focused on selected influential parameters, namely temperature, residence time, and density, while the four-point bending strength is obtained as the output parameter. The obtained experimental data are stochastically modeled and compared with the model created by genetic programming (GP). The classical mathematical analysis obtained treatment parameters in relation to the maximum bending strength (T = 187 °C, t = 125 min ρ = 0.780 g/cm3) and compared with the results obtained by genetic algorithm (GA) (T = 208 °C, t = 122 min, and ρ = 0.728 g/cm3). It is possible to obtain models that describe experimental results well with stochastic modeling and evolutionary algorithms.

Insights into Optimal Schemes of Radial Network Arch Pedestrian Bridge

The main objective of the article is to find the optimal ranges of the cardinal topological, shape, and dimensional parameters that fully describe the constructional scheme of pedestrian radial network arch bridges of moderate spans. This task is solved by formulating the global optimization problem and seeking the minimum mass of the whole bridge structure. An optimal bridge scheme was obtained by tuning a large set of interdependent design parameters of diverse character: the topological parameters–the type of hanger arrangement, and the number of hangers; the shape parameters–hanger spread and central section angles, the alteration of these angles, and the arch rise; and the sizing parameters–all cross‐sectional dimensions of structural members. Mathematically, this is a constrained mixed‐integer global optimization problem solved by a stochastic evolutionary algorithm. Plane light‐deck bridges of typical moderate 30, 45, 60, 75, and 90 m spans were optimized. Decisive design parameters and their rational ranges for all spans were revealed. In addition, the effects of some simplifications of the general bridge scheme were shown: using the constant spread and central section angles; imposing certain values on the ratio arch rise/bridge span; bounding the hanger diameters to a given value; and so on. Obtained results clearly indicate that the design recommendations for the lightweight network arch bridges should differ from the recommendations for similar automotive and railway bridges. Our findings are: the optimal ratio of arch rise to the bridge span is 0.20–0.30, the number of hangers is >40 even for the shortest spans, the spread angle between hangers is 30°–40°, and the girder mass amounts to ∼30% of the total bridge mass, while the mass of hangers amounts up to 20% and increases when imposing constraints on the minimal radius at the expense of diminishing arch mass.

Parametric study on mass minimization of radial network arch pedestrian bridges

This article deals with the optimization of radial network arch bridges for pedestrian traffic. This is a challenging optimization problem that involves determining certain topological parameters, as well as shape configurations and all sizing parameters of structural members, in order to seek minimum mass. An optimal bridge scheme was sought tuning a large set of design parameters of diverse character: the type of hanger arrangement, the number of hangers, their inclination and spread angles, the alteration of these angles, the arch rise, etc. Mathematically, the optimization of the bridge scheme is a mixed-integer constrained global optimization problem solved employing a stochastic evolutionary algorithm. Plane light-deck bridges of 60 m span were optimized using the proposed optimization technique. Decisive design parameters and their promising ranges were revealed. In addition, the influence of some simplified effects was shown: using constant spread and inclination angles, limiting the diameters of the hangers to a given value, etc. The results show that the available design recommendations for automotive and railway network arch bridges are not fully applicable to similarly designed pedestrian bridges.

Интеллектуальный робастный регулятор на технологиях когнитивных вычислений. Ч. 1: Модели когнитивного управления с эмоциональным обучением мозга

n on-linecontrol and decision-making systems, emotional brain training is a preferred methodology (compared to stochastic gradient-based and evolutionary algorithms) due to its low computational complexity and fast robust learning. To describe the emotional learning of the brain, a mathematical model was created —the brain emotional learning controller (BELC). The design of intelligent systems based on emotional signals basedoncontrol methods assoft computing technologies: artificial neural networks, fuzzy control and genetic algorithms. Based on the simulated mathematical model of mammals BEL, a controller architecture has been developed. Applied approachcalled “Brain Emotional Learning Based Intelligent Controller” (BELBIC) —a neurobiologically motivated intelligent controller based on a computational model of emotional learning in the mammalian limbic system. The article describes applied models of intelligent regulators based on emotional learning of the brain. BELBIC's learning capabilities;versatility and low computational complexity make it a very promising toolkitfor on-lineapplications.

Modified Algorithms for Fast Construction of Optimal Latin-Hypercube Design

As accuracy of optimization can not be guaranteed without high-quality samples, the distribution of a finite number of evaluation points where experiments should be conducted in design space is an important issue, particularly when the experiment to obtain sample is expensive. To utilize limited number of evaluation points to represent the design space, optimal latin-hypercube design (OLHD), with considerable space-filling quality, is widely used as a methodology in design of experiments (DOE). However, OLHD generation requires significant time. This study focuses on further improvement of efficiency in generation of OLHD in terms of both time and latin-hypercube design (LHD) optimization. Two modified algorithms, namely the modified enhanced stochastic evolutionary (MESE) and translational propagation modified enhanced stochastic evolutionary (TPMESE) algorithms, based on existing algorithms, are proposed. The MESE algorithm is modified from the enhanced stochastic evolutionary (ESE) algorithm by using a new update method for “temperature,” while the TPMESE algorithm optimizes the LHD via translational propagation (TPLHD) instead of optimizing a random LHD like the MESE algorithm does. Their performance is evaluated by comparison with several famous heuristic algorithms and each original algorithm using optimization tests of LHDs with various sizes. For all cases, proposed algorithms show better performance of convergence than other heuristic algorithms participated in our comparison. For large and medium LHDs, the MESE algorithm faster converges to a solution with the same level as original algorithm (ESE). For large LHDs, the TPMESE algorithm is the most time efficient algorithm in obtaining near-optimal or sufficient near-optimal designs.

A Rough Set Pooled Fitness Function Based Particle Swarm Optimization Algorithm using Golden Ratio Principle for Feature Selection

Particle Swarm Optimization, a nature based stochastic evolutionary algorithm that iteratively tries to improvise the solution pertaining to a particular objective function. The problem becomes challenging if the objective function is not properly identified nor it is properly been evaluated which results in slow convergence and inability to find the optimal solution. Hence, we propose a novel rough set based particle swarm optimization algorithm using golden ratio principle for an efficient feature selection process that focusses on two objectives: First, that results in a reduced subset of features without conceding the originality of the data and the second is that yields a high optimal result. Since many subset of features might result with a meaningful solution, we have used the golden ratio principle to extract the most reduced subset with a high optimal solution. The algorithm has been tested over several benchmark datasets. The results shows that the proposed algorithm identifies a small set of features without convincing the optimal solution, thus able to achieve the stated objectives.

Multi-level Hybridized Optimization Methods Coupling Local Search Deterministic and Global Search Evolutionary Algorithms

Efficient optimization methods coupling a stochastic evolutionary algorithm with a gradient based deterministic method are presented in this paper. Two kinds of hybridization are compared: one is a stochastic/deterministic alternate algorithm, the other is a stochastic/deterministic embedded algorithm. In the alternating algorithm, stochastic and deterministic optimizers are performed alternately as follows: some individuals are selected from the previous population, and sent to the deterministic algorithms for further optimization, then the improved individuals are inserted into the above population to form a new one for the stochastic algorithm. In the embedded hybridized algorithm, stochastic and deterministic optimization software are run in parallel and independently, the coupling between them is that the deterministic optimizer operates on a randomly selected individual (or the best individual) from the non evaluated population of the stochastic algorithm, then its outcome (new individual) is re-injected into the evaluated population. Moreover, a multilevel approximation (e.g. variable fidelity modeling, analysis and hierarchical approximated parameterization) is introduced in the algorithm, via a low fidelity modeling and rough parameterization to perform a search on large population at lower level, and a high fidelity modeling with detailed parameterization used at higher level. After a theoretical validation of the methods on mathematical test cases, the hybridized methods are successfully applied to the aerodynamic shape optimization of a fore-body of an hypersonic air breathing vehicle, providing both a significant acceleration in terms of parallel HPC performance and improved quality of the design.

Whale optimization algorithm applied to load frequency control of a mixed power system considering nonlinearities and PLL dynamics

In this work, an attempt has been made to implement a nature-inspired stochastic evolutionary algorithm, namely whale optimization algorithm (WOA) for exploring optimum and practical solutions of load frequency control (LFC) problem in power system. The proposed WOA mimics the ‘bubble-net feeding’ strategy of ‘humpback whales’ in the oceans. The optimization technique is individually applied to a two-area thermal power plant and two-area hydro-thermal-gas power plant with AC–DC tie-line for fine-tuning of the controller parameters. The study further houses the consequences of frequency measurement and the dynamics of a phase-locked loop (PLL) with power system nonlinearities. To establish the efficacy of WOA, the obtained results are compared with results of success history based adaptive differential evolution (SHADE), krill herd algorithm (KHA), and some other well-known control algorithms. Finally, statistical analysis is performed to affirm robustness of the proposed WOA in LFC area.

Multiobjective Collective Decision Optimization Algorithm for Economic Emission Dispatch Problem

The collective decision optimization algorithm (CDOA) is a new stochastic population‐based evolutionary algorithm which simulates the decision behavior of human. In this paper, a multiobjective collective decision optimization algorithm (MOCDOA) is first proposed to solve the environmental/economic dispatch (EED) problem. MOCDOA uses three novel learning strategies, that is, a leader‐updating strategy based on the maximum distance of each solution in an external archive, a wise random perturbation strategy based on the sparse mark around a leader, and a geometric center‐updating strategy based on an extreme point. The proposed three learning strategies benefit the improvement of the uniformity and the diversity of Pareto optimal solutions. Several experiments have been carried out on the IEEE 30‐bus 6‐unit test system and 10‐unit test system to investigate the performance of MOCDOA. In terms of extreme solutions, compromise solution, and three metrics (SP, HV, and CM), MOCDOA is compared with other existing multiobjective optimization algorithms. It is demonstrated that MOCDOA can generate the well‐distributed and the high‐quality Pareto optimal solutions for the EED problem and has the potential to solve the multiobjective optimization problems of other power systems.

On the application of a nature-inspired stochastic evolutionary algorithm to constrained multi-objective beer fermentation optimisation

Fermentation is an essential step in beer brewing, often acting as the system bottleneck due to the time-consuming nature of the process stage (duration >120h), where a trade-off exists between attainable ethanol concentration and required batch time. To explore this trade-off we employ a multi-objective plant propagation algorithm (the Strawberry algorithm), for identifying temperature manipulations for improved fermentation performance. The methodology employed successfully produces families of favourable temperature profiles which exist along the Pareto front. A subset of these output profiles can simultaneously reduce batch time and increase product ethanol concentration while satisfying constraints on by-products produced in the fermenters, representing significant improvements in comparison with current industrial practice. A potential batch time reduction of over 12h has been highlighted, coupled with a moderate improvement in ethanol content.

A novel extension algorithm for optimized Latin hypercube sampling

ABSTRACTLatin hypercube sampling (LHS), as an efficient sampling method, has been widely used in computer experiments. But it is difficult to choice the sample size while applying LHS, especially for expensive simulations. The effective way is to add sample points sequentially. Nevertheless, the oversampling problem may be countered while extending the sample size with the existing extension algorithms of LHS. To alleviate this problem and obtain extension sample with good space-filling properties, a novel extension algorithm of optimized LHS (OLHS) is proposed. According to the extending rule, a new LHS is constructed by adding sample points of size n each time firstly. Then each additional sample points are optimized by the enhanced stochastic evolutionary algorithm based on the space-filling criterion. The extension algorithm is illustrated by two test functions and appears to perform well in both efficiency and convergence compared with the traditional extension algorithm.

Parameter Study on Weight Minimization of Network Arch Bridges

The article concerns optimization of network arch bridges. This is challenging optimization problem involving even for conventional scheme of network arch bridge the identification of some topological parameters as well as shape configurations and all sizing parameters of structural members, seeking the minimum weight. Optimal bridge scheme is sought tuning a large set of design parameters of diverse character: the type of hanger arrangement, the number of hangers, their inclination angles and placement distances, the arch shape and rise, etc. Mathematically, the optimization of the bridge scheme is a mixed-integer constrained global optimization problem solved employing stochastic evolutionary algorithm. Plane heavy/moderate/and light-deck bridges of 18, 30, 42 and 54 m spans were optimized using proposed optimization technique. The decisive design parameters and their promising ranges were revealed. Also, the influence of some simplifications is shown: changing the arch shape from elliptical to circular, placing the hangers at equal distances, etc.

Opposition-based differential evolution for hydrothermal power system

This paper presents opposition-based differential evolution to determine the optimal hourly schedule of power generation in a hydrothermal system. Differential evolution (DE) is a population-based stochastic parallel search evolutionary algorithm. Opposition-based differential evolution has been used here to improve the effectiveness and quality of the solution. The proposed opposition-based differential evolution (ODE) employs opposition-based learning (OBL) for population initialization and also for generation jumping. The effectiveness of the proposed method has been verified on two test problems, two fixed head hydrothermal test systems and three hydrothermal multi-reservoir cascaded hydroelectric test systems having prohibited operating zones and thermal units with valve point loading. The results of the proposed approach are compared with those obtained by other evolutionary methods. It is found that the proposed opposition-based differential evolution based approach is able to provide better solution.

Rigorous Integrated Evolutionary Workflow for Optimal Exploitation of Unconventional Gas Assets

Applications of stochastic evolutionary algorithms in engineering are gaining more attention in practical applications in the oil and gas industry. An important factor to consider when implementing stochastic algorithms is its ability to find the global optimum efficiently. In this study the authors formulate, implement, and test a genetic algorithm with strong elitism to solve a critical problem in the upstream oil industry: how to develop economically an unconventional gas asset. This problem involves finding the optimal number of horizontal wells, the number of transverse hydraulic fracture stages along them, and stage half-length. The described problem is inherently discrete or mixed optimization problem for which the authors develop a conceptually new evolutionary integrated framework that addresses all production design questions. They outline the range of applicability of their workflow and provide ample test cases and results. Their rigorous formulation performs well for a given problem statement and finds the optimal solution that is consistent with the industry accepted optimum.

An Implementation of Self-Organizing Maps for Airfoil Design Exploration via Multi-Objective Optimization Technique

Design candidates obtained from optimization techniques may have meaningful information, which provides not only the best solution, but also a relationship between object functions and design variables. In particular, trade-off studies for optimum airfoil shape design involving various objectives and design variables require the effective analysis tool to take into account a complexity between objectives and design variables. In this study, for the multiple-conflicting objectives that need to be simultaneously fulfilled, the real-coded Adaptive Range Multi-Objective Genetic Algorithm code, which represents the global and stochastic multi-objective evolutionary algorithm, was developed for an airfoil shape design. Furthermore, the PARSEC method reflecting geometrical properties of airfoil is adopted to generate airfoil shapes. In addition, the Self-Organizing Maps, based on the neural network, are used to visualize trade-offs of a relationship between the objective function space and the design variable space obtained by evolutionary computation. The Self-Organizing Maps that can be considered as data mining of the engineering design generate clusters of object functions and design variables as an essential role of trade-off studies. The aerodynamic data for all candidate airfoils is obtained through Computational Fluid Dynamics. Lastly, the relationship between the maximum lift coefficient and maximum lift-to-drag ratio as object functions and 12 airfoil design parameters based on the PARSEC method is investigated using the Self-Organizing Maps method.

Hybrid evolutionary search for the minimum sum coloring problem of graphs

Given a graph G, a proper k-coloring of G is an assignment of k colors {1,…,k} to the vertices of G such that two adjacent vertices receive two different colors. The minimum sum coloring problem (MSCP) is to find a proper k-coloring while minimizing the sum of the colors assigned to the vertices. This paper presents a stochastic hybrid evolutionary search algorithm for computing upper and lower bounds of this NP-hard problem. The proposed algorithm relies on a joint use of two dedicated crossover operators to generate offspring solutions and an iterated double-phase tabu search procedure to improve offspring solutions. A distance-and-quality updating rule is used to maintain a healthy diversity of the population. We show extensive experimental results to demonstrate the effectiveness of the proposed algorithm and provide the first landscape analysis of MSCP to shed light on the behavior of the algorithm.

Design of Large Thinned Arrays Using Different Biogeography-Based Optimization Migration Models

Array thinning is a common discrete-valued combinatorial optimization problem. Evolutionary algorithms are suitable techniques for above-mentioned problem. Biogeography-Based Optimization (BBO), which is inspired by the science of biogeography, is a stochastic population-based evolutionary algorithm (EA). The original BBO uses a linear migration model to describe how species migrate from one island to another. Other nonlinear migration models have been proposed in the literature. In this paper, we apply BBO with four different migration models to five different large array design cases. Additionally we compare results with five other popular algorithms. The problems dimensions range from 150 to 300. The results show that BBO with sinusoidal migration model generally performs better than the other algorithms. However, these results are considered to be indicative and do not generally apply to all optimization problems in antenna design.