Hybrid Evolutionary Optimization Algorithm Research Articles

Robust optimization of a marine current turbine using a novel robustness criterion

The present paper aims to establish a systematic robust optimization framework for the hydrodynamic performance of marine current turbines against uncertain conditions. To this end, we developed a novel robustness criterion based on the cumulative distribution function and employed an XFOIL-based blade element momentum method to predict turbine performance. The non-intrusive polynomial chaos expansion was also applied for uncertainty propagation and probabilistic computations. Initially, a set of sensitivity analysis studies was conducted on a model turbine to determine the most influential parameters. The results indicated that uncertainties have a significant impact on turbine performance, even under controlled experimental conditions. In the next stage, we applied deterministic and robust optimization approaches to optimize a real-world marine turbine design under practical conditions. We utilized a hybrid evolutionary optimization algorithm based on the Genetic Algorithm and Particle Swarm Optimization. Additionally, the novel robustness criterion was introduced to address the issues associated with conventional criteria. The robust optimization was performed independently using three criteria: the worst-case scenario, the mean value penalty, and the novel criterion. By investigating the probabilistic characteristics of the performance in the deterministic and robust optimum turbines, we demonstrated that the power generation capacity of the deterministic optimum is expectedly higher than that of the robust optimum in deterministic hypothetical conditions. However, during turbine operation in real conditions, which are accompanied by large uncertainties, the optimum design achieved using the new robustness criterion is more likely to produce greater power. Furthermore, the power variations of the turbine are more limited, indicating increased reliability and stability of the optimized design.

Hybrid evolutionary robust optimization-based optimal control for time-delay nonlinear systems

Optimal controls receive much attention owing to their remarkable performance for nonlinear systems. However, unknown time-delay disturbances in the optimal control process make it difficult to find high-quality optimal set points. To address this problem, a hybrid evolutionary robust optimization-based optimal control (HERO-OC) method is proposed to reduce the negative effect of time-delay disturbances and enhance the performance of optimal set points. First, a data-driven time-delay disturbance observer (DTDO), based on fuzzy neural networks, is designed to describe the time-delay disturbances of optimal objectives. Then, the expressions and intervals of time-delay disturbances can be estimated to improve the accuracy of optimal objectives. Second, a hybrid evolutionary robust optimization (HERO) algorithm, which combines an expectation robust optimization strategy with a min–max evolutionary robust optimization strategy, is proposed to solve optimal set points. Then, the average robustness and conservative of robustness optimal set points can be improved. Third, an adaptive time-delay controller using Lyapunov–Krasovskii functionals is proposed to track optimal set points. Then, control accuracy is improved while ensuring control stability. Finally, the superior performance of HERO-OC is compared with some novel optimal control methods in a second-order nonlinear system and a wastewater treatment process.

Stochastic Multi-Objective Optimized Dispatch of Combined Cooling, Heating, and Power Microgrids Based on Hybrid Evolutionary Optimization Algorithm

The penetration of distributed power sources has been increasing with the continuous promotion of clean renewable energy sources. This paper seeks to improve the utilization rate of clean energy and reduce the cost of microgrid operation by first establishing a double-layer wind power prediction error model based on a comprehensive consideration of the time-of-use price and the operating characteristics of different types of clean energy sources, such as wind power, photovoltaic power, thermal power, and transmission tie lines. A combined cooling, heating, and power microgrid collaborative optimization model that considers wind power forecast uncertainty is established with the goal of minimizing economic cost, environmental cost, and degree of power-generation unit output asynchrony of the microgrid. The established multi-objective optimization model is solved using an improved intelligent optimization algorithm that combines the non-dominated sorting genetic algorithm (NSGA) with co-evolution theory and the beetle antennae search algorithm. This algorithm employs a variety of groups in the NSGA to help with correcting the approximations of group members through competition and cooperation. Therefore, the proposed algorithm can combine the excellent convergence of the NSGA and the powerful searching ability of co-evolutionary algorithms. Finally, a practical microgrid system in Northwest China is simulated as a case study, and the performance of the proposed algorithm is compared with that of the conventional NSGA. The simulation results demonstrate the superiority of the global search performance and the rapid convergence performance of the proposed hybrid algorithm.

Designing foam filled sandwich panels for blast mitigation using a hybrid evolutionary optimization algorithm

Developing sandwich structures with high energy absorption capability is important for shock loading applications. In the present study, a hybrid evolutionary optimization technique based on Multi-Island Genetic Algorithm and Hooke-Jeeves Algorithm is used in the design stage of the sandwich structures to obtain effective results. Optimum parameters of cell geometry were investigated using the hybrid optimization algorithm to design foam filled sandwich panels for three main boundary conditions. Shock tube experiments were conducted in order to simulate the shock load effects along with 3D and 2D finite element analysis. Using the experimental results, a simulation-based design optimization approach was prepared and used to develop the designs of new sandwich structures. Promising results were obtained for all three different boundary conditions. In the simply supported case, 21% improvement of shock absorption was achieved by using 57% less volume of foam with respect to the original fully foam filled sandwich panel. In the clamped-clamped case, 16% improvement of shock absorption with 52% less volume was obtained. In the rigid base case study, 6% improvement of shock absorption with 38% less volume usage was achieved. The structures developed in this study will be of use in the defense, automotive and other industries.

Inversion method based on stochastic optimization for particle sizing.

A stochastic inverse method is presented based on a hybrid evolutionary optimization algorithm (HEOA) to retrieve a monomodal particle-size distribution (PSD) from the angular distribution of scattered light. By solving an optimization problem, the HEOA (with the Fraunhofer approximation) retrieves the PSD from an intensity pattern generated by Mie theory. The analyzed light-scattering pattern can be attributed to unimodal normal, gamma, or lognormal distribution of spherical particles covering the interval of modal size parameters 46≤α≤150. The HEOA ensures convergence to the near-optimal solution during the optimization of a real-valued objective function by combining the advantages of a multimember evolution strategy and locally weighted linear regression. The numerical results show that our HEOA can be satisfactorily applied to solve the inverse light-scattering problem.

Metaheuristic profiling to assess performance of hybrid evolutionary optimization algorithms applied to complex wellbore trajectories

Metaheuristic profiling is proposed as an effective technique with which to evaluate the relative contributions of the metaheuristic components of hybrid evolutionary optimization algorithms in progressing searches of feasible solution spaces to locate global optimum values of their objective functions. Although many useful evolutionary algorithms have been successfully proposed and tested to solve a wide range of complex mathematical optimization problems, when applied to real-world optimization tasks their performance can often be improved by hybridization with other metaheuristics. A case is made here that in developing optimization algorithms for specific practical applications it is better to treat the available evolutionary algorithms as part of a “toolbox” of metaheuristic components that can be configured in various hybridized combinations. The technique of metaheuristic profiling is evaluated as means of identifying the relative contributions of individual metaheuristic components in contributing to the discovery of optimum solutions over multiple iterations of hybrid algorithms. The metaheuristic profiling technique of a toolbox of metaheuristic components is evaluated in terms of applying seven hybrid evolutionary algorithms to optimize a previously studied complex well-bore trajectory optimization problem. The seven hybrid evolutionary algorithms developed with multiple metaheuristics are built upon standard: genetic; particle swarm; bee colony; ant colony; harmony search, cuckoo search and bat flight algorithms. Pseudocode for each of the hybrid algorithms studied are provided in an appendix. These codes identify the metaheuristics included and the sequence in which they are applied in the hybrid algorithms. All seven hybrid algorithms are coded in VBA based in Microsoft Excel with the assistance of the metaheuristic profiling technique, to provide reliably reproducible solutions to well-bore trajectory design optimization. Analysis of metaheuristic performance also confirms the benefits of fat-tailed distributions, sampled chaotically, in a novel way, to drive certain metaheuristics.

Impact of Number of Interactions, Different Interaction Patterns, and Human Inconsistencies on Some Hybrid Evolutionary Multiobjective Optimization Algorithms*

ABSTRACTWe investigate the impact of the number of human–computer interactions, different interaction patterns, and human inconsistencies in decision maker responses on the convergence of an interactive, evolutionary multiobjective algorithm recently developed by the authors. In our context “an interaction” means choosing the best and worst solutions among a sample of six solutions. By interaction patterns we refer to whether preference questioning is more front‐, center‐, rear‐, or edge‐loaded. As test problems we use two‐ to four‐objective knapsack problems, multicriteria scheduling problems, and multiobjective facility location problems. In the tests, two different preference functions are used to represent actual decision maker preferences, linear and Chebyshev. The results indicate that it is possible to obtain solutions that are very good or even nearly optimal with a reasonable number of interactions. The results also indicate that the algorithm is robust to minor inconsistencies in decision maker responses. There is also surprising robustness toward different patterns of interaction with the decision maker. The results are of interest to the evolutionary multiobjective (EMO) community actively developing hybrid interactive EMO approaches.

Reconstruction of the wavefront aberration from real interferometric data using a hybrid evolutionary optimization algorithm with Zernike polynomials

This paper describes the use of a hybrid evolutionary optimization algorithm (HEOA) for computing the wavefront aberration from real interferometric data. By finding the near-optimal solution to an optimization problem, this algorithm calculates the Zernike polynomial expansion coefficients from a Fizeau interferogram, showing the validity for the reconstruction of the wavefront aberration. The proposed HEOA incorporates the advantages of both a multimember evolution strategy and locally weighted linear regression in order to minimize an objective function while avoiding premature convergence to a local minimum. The numerical results dem- onstrate that our HEOA is robust for analyzing real interferograms degraded by noise. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. (DOI: 10.1117/1.OE.54.9.094102)

Hybrid Evolutionary Optimization Algorithm for Structures

In the structure problems, the randomness and the uncertainties of the distribution of the structural parameters are a crucial problem. In the case of optimization of structure, the objective is to play a dominant role in the structural optimization problem introducing the reliability concept. The optimization of the initial structural cost under constraints imposed on the values of elemental reliability indices corresponding to various limit states. In this paper we use a new optimization method for a modified particle swarm optimization algorithm (MPSO) combined with a simulated annealing algorithm (SA). MPSO is known as an efficient approach with a high performance of solving optimization problems in many research fields. It is a population intelligence algorithm inspired by social behavior simulations of bird flocking. Numerical results show the robustness of the MPSO-SA algorithm.

A hybrid evolutionary multiobjective optimization algorithm with adaptive multi-fitness assignment

There are several studies on hybrid multi-operator recombination methods, while few works have been proposed in the area of combining different fitness assignment in a framework. On the other hand, it is known that fitness assignment has a marked impact on the performance of evolutionary multiobjective optimization algorithm (EMOA). In this paper, a hybrid EMOA is proposed, which divides the population into several smaller subpopulations according to their distribution in the objective space. Each subpopulation is evolved by an individual EMOA, and a hybrid performance measure estimates the performance of these EMOAs. We focus on the fitness assignment and assume that all EMOAs used in the subpopulations adopt the same recombination operator. To evaluate performance of the proposed algorithm, we compare it with MOEA/D-M2M, MOE-A/D, SMS-EMOA and NSGA-II on 16 test instances. Experimental results show that the proposed algorithm performs better than or similar to those compared EMOAs.

Efficient hybrid evolutionary algorithm for optimization of a strip coiling process

This article proposes an efficient metaheuristic based on hybridization of teaching–learning-based optimization and differential evolution for optimization to improve the flatness of a strip during a strip coiling process. Differential evolution operators were integrated into the teaching–learning-based optimization with a Latin hypercube sampling technique for generation of an initial population. The objective function was introduced to reduce axial inhomogeneity of the stress distribution and the maximum compressive stress calculated by Love's elastic solution within the thin strip, which may cause an irregular surface profile of the strip during the strip coiling process. The hybrid optimizer and several well-established evolutionary algorithms (EAs) were used to solve the optimization problem. The comparative studies show that the proposed hybrid algorithm outperformed other EAs in terms of convergence rate and consistency. It was found that the proposed hybrid approach was powerful for process optimization, especially with a large-scale design problem.

Structural identification (St-Id) using finite element models for optimum sensor configuration and uncertainty quantification

Developments and advances in experimental technologies providing useful data make it possible to identify civil engineering structures and to obtain a more reliable model characterizing the existing condition for decision making. Analytical models such as Finite Element (FE) models, which are calibrated using structural health monitoring (SHM) data, better represent the existing structures' behavior under different loading conditions. However, the SHM data should include sufficient information about the structural parameters to be identified. In this study, a novel methodology is proposed in order to determine the optimum sensor configuration which provides adequate data for structural identification (St-Id). The success of the St-Id is investigated in a comparative fashion by comparing the model parameters calibrated using different sensor configurations. Uncertainties both in the mathematical model and the experimental data are taken into account using the fuzzy number concept. A so-called inverse fuzzy arithmetic technique is used to quantify the uncertainties in the updated parameters. The proximity of linkage values, which are the product of cluster analysis, is used to determine the optimal sensor configuration. The optimal sensor configuration is then verified by using the relative amount of uncertainty in the updating parameters resulting from the inverse propagation of the uncertainties. A hybrid evolutionary optimization algorithm is also proposed in order to efficiently minimize an objective function that consists of differences between the fuzzy experimental measurements and the analytical data. Genetic Algorithms (GA) and Harmony Search (HS) algorithm are combined to enhance the efficiency and the robustness of the optimization process. An analytical benchmark bridge structure developed for bridge health monitoring studies is used as the test structure to verify the proposed methodologies. Three different cases including the undamaged and the damage cases are considered. It has been shown that there is no significant difference between the St-Id results obtained by using a dense sensor configuration and the optimum configuration obtained by the proposed method in terms of accuracy.

A Hybrid Optimization Algorithm Based on Cuckoo Search and PSO for Data Clustering

Data clustering as one of the important data mining techniques is a fundamental and widely used method to achieve useful information about data. The purpose of clustering is to group together data points, which are close to one another. In face of the clustering problem, clustering methods still suffer from trapping in a local optimum and cannot often find global clusters. In order to overcome the shortcoming of the available clustering methods, this paper presents a hybrid clustering algorithm. This paper is presented an efficient hybrid evolutionary optimization algorithm based on combining Particle Swarm Optimization (PSO) and Cuckoo Search (CS), called CS-PSO, for optimally clustering N object into K clusters. The new CS-PSO algorithm is tested on several data sets, and its performance is compared with those of GA, FCM, Fuzzy-PSO and K-means clustering. The simulation results show that the new method carries out better results than the Genetic algorithm (GA), K-means, Fuzzy C-means FCM), Genetic-K means and Fuzzy-PSO.

A Hybrid Framework for Evolutionary Multi-Objective Optimization

Evolutionary multi-objective optimization algorithms are widely used for solving optimization problems with multiple conflicting objectives. However, basic evolutionary multi-objective optimization algorithms have shortcomings, such as slow convergence to the Pareto optimal front, no efficient termination criterion, and a lack of a theoretical convergence proof. A hybrid evolutionary multi-objective optimization algorithm involving a local search module is often used to overcome these shortcomings. But there are many issues that affect the performance of hybrid evolutionary multi-objective optimization algorithms, such as the type of scalarization function used in a local search and frequency of a local search. In this paper, we address some of these issues and propose a hybrid evolutionary multi-objective optimization framework. The proposed hybrid evolutionary multi-objective optimization framework has a modular structure, which can be used for implementing a hybrid evolutionary multi-objective optimization algorithm. A sample implementation of this framework considering NSGA-II, MOEA/D, and MOEA/D-DRA as evolutionary multi-objective optimization algorithms is presented. A gradient-based sequential quadratic programming method as a single objective optimization method for solving a scalarizing function used in a local search is implemented. Hence, only continuously differentiable functions were considered for numerical experiments. The numerical experiments demonstrate the usefulness of our proposed framework.

Finite element based hybrid evolutionary optimization approach to solving rigid pavement inversion problem

This paper focuses on the development of a new backcalculation method for concrete road structures based on a hybrid evolutionary global optimization algorithm, namely shuffled complex evolution (SCE). Evolutionary optimization algorithms are ideally suited for intrinsically multi-modal, non-convex, and discontinuous real-world problems such as pavement backcalculation because of their ability to explore very large and complex search spaces and locate the globally optimal solution using a parallel search mechanism as opposed to a point-by-point search mechanism employed by traditional optimization algorithms. SCE, a type of evolutionary optimization algorithms based on the tradeoff of exploration and exploitation, has proved to be an efficient method for many global optimization problems and in some cases it does not suffer the difficulties encountered by other evolutionary computation techniques. The SCE optimization approach is hybridized with a neural networks surrogate finite-element based forward pavement response model to enable rapid computation of global or near-global pavement layer moduli solutions. The proposed rigid pavement backcalculation model is evaluated using field non-destructive test data acquired from a full-scale airport pavement test facility.

An Efficient Hybrid Evolutionary Optimization Algorithm combining Ant Colony Optimization with Simulated Annealing

This paper has proposed an approach based on ACO-SA algorithm for the multi-objective Distribution Feeder Reconfiguration. The simulation results have shown that global or close to global optimum solutions for the system losses, the voltage deviation was attained. Also, the proposed approach minimizes the number of switching operations. The proposed method is independent on the initial status of network switches. The ACO-SA is a combination of two powerful optimization algorithms: Ant Colony Optimization and Simulated Optimization.

Application and comparison of hybrid evolutionary multiobjective optimization algorithms for solving task scheduling problem on heterogeneous systems

Task scheduling problem in heterogeneous systems (TSPHS) is a multiobjective optimization problem (MOP). Multiobjective evolutionary algorithms (MOEA) are well suited for solving multiobjective task scheduling problem. In this paper, the two conflicting objectives namely, makespan and reliability are considered. The performance of MOEAs can be improved by hybridization with local search. Hybridization of MOEAs improves the convergence speed to Pareto front. Simple neighborhood search (SNS) algorithm is used as the local search algorithm. The weighted-sum based approach for solving the MOP with its hybrid version is compared. Then the two MOEAs: SPEA2 and NSGA-II are compared with each other in the pure and hybrid version for random task graphs and also for a real-time numerical application graph. The simulations confirm that Hybrid NSGA-II is best suited for solving the task scheduling problem.

An efficient hybrid algorithm based on modified imperialist competitive algorithm and K-means for data clustering

Clustering techniques have received attention in many fields of study such as engineering, medicine, biology and data mining. The aim of clustering is to collect data points. The K-means algorithm is one of the most common techniques used for clustering. However, the results of K-means depend on the initial state and converge to local optima. In order to overcome local optima obstacles, a lot of studies have been done in clustering. This paper presents an efficient hybrid evolutionary optimization algorithm based on combining Modify Imperialist Competitive Algorithm (MICA) and K-means (K), which is called K-MICA, for optimum clustering N objects into K clusters. The new Hybrid K-ICA algorithm is tested on several data sets and its performance is compared with those of MICA, ACO, PSO, Simulated Annealing (SA), Genetic Algorithm (GA), Tabu Search (TS), Honey Bee Mating Optimization (HBMO) and K-means. The simulation results show that the proposed evolutionary optimization algorithm is robust and suitable for handling data clustering.

Global Optimization of Pavement Structural Parameters during Back-Calculation Using Hybrid Shuffled Complex Evolution Algorithm

In this paper, the use of a hybrid evolutionary optimization algorithm is proposed for global optimization of pavement structural parameters through inverse modeling. Shuffled complex evolution (SCE) is a population-based stochastic optimization technique combining the competitive complex evolution with the controlled random search, the implicit clustering, and the complex shuffling. Back-calculation of pavement layer moduli is an ill-posed inverse engineering problem, which involves searching for the optimal combination of pavement layer stiffness solutions in an unsmooth, multimodal, complex search space. SCE is especially considered a robust and efficient approach for global optimization of multimodal functions. A desirable characteristic of the SCE algorithm is that it uses information about the nature of the response surface, extracted using the deterministic Simplex geometric shape, to direct the search into regions with higher posterior probability. The hybrid back-calculation system described in this paper combines the robustness of the SCE in global optimization with the computational efficiency of neural networks and advanced pavement system characterization offered by employing finite-element models. This is the first time the SCE approach is applied to real-time nondestructive evaluation of pavement systems required in the routine maintenance and rehabilitation activities for sustainable transportation infrastructure.

Hybrid evolutionary optimization algorithm MPSO-SA

This paper proposes a new method for a modified particle swarm optimization algorithm (MPSO) combined with a simulated annealing algorithm (SA). MPSO is known as an efficient approach with a high performance of solving optimization problems in many research fields. It is a population intelligence algorithm inspired by social behavior simulations of bird flocking. Considerable research work on classical method PSO (Particle Swarm Optimization) has been done to improve the performance of this method. Therefore, the proposed hybrid optimization algorithms MPSO- SA use the combination of MPSO and simulated annealing SA. This method has the advantage to provide best results comparing with all heuristics methods PSO and SA. In this matter, a benchmark of eighteen well-known functions is given. These functions present different situations of finding the global minimum with gradual difficulties. Numerical results presented, in this paper, show the robustness of the MPSO-SA algorithm. Numerical comparisons with these three algorithms: Simulated Annealing, Modified Particle swarm optimization and MPSO-SA prove that the hybrid algorithm offers best results.