Hierarchical Fair Competition Research Articles

Analysis of new differential evolution variants to solve multi-modal problems

Differential evolution algorithm requires diversified population to solve multi- modalproblems. DE supports non-distributed population. Differential evolution algorithm (DE) versions include differential evolution algorithm with best selection method and species evolution (DESBS) and differential evolution algorithm with hierarchical fair competition modal (HFCDE). This article analyzed the efficiency of HFCDE and DESBS to solve the multi-modal s’ problems. HFCDE and DESBS support non-distributed population structured. HFCDE starts with set of feasible solution then it distributed them in the different hierarchy. HFCDE provides the fair competition. DESBS is another semi distributed, differential evolution algorithm.It starts with set of feasible solutions (population). Later it identifies the niches and create the sub-groups within population. Both HFCDE and DESBS have outperformed the other variant of state-of-art variants of DE. Here, the performance of DESBS and the HFCDE are rigorously tested on the multimodal problems. The success of DESBS over HFCDE in multi-modal difficulties managed to overcome the phenomena of elitism to resolve the complex problems, it has been observed that DESBS performs better than HFCDE in complex multi-modal scenarios.

Differential Evolution Algorithm with Hierarchical Fair Competition Model

This paper presents the study of differential evolution algorithm with hierarchical fair competition model (HFC-DE). HFC model is based on the fair competition of societal system found in natural world. In this model, the population is split into hierarchy and the competition is allowed between the hierarchical members. During evolution, the population members are allowed to move within the hierarchy levels. The standard differential evolution algorithm is used for population evolution. Experimentation has carried out to define the parameter for proposed model on test suit having unimodal problems and multi-model problems. After analyzing the results, the two variants of HFC-DE are proposed, named hierarchical fair competition model in differential evolution algorithm with replacement, I,e, HFCDE-R and hierarchical fair competition model in differential evolution algorithm without replacement, I,e, HFCDE-wR. The problem-solving capabilities of both algorithms are checked and the results are compared with differential evolution algorithm (DE) and other variants of DE. The early results are encouraging and motivating.

Hierarchical fair competition algorithm for optimization of reliability of complex electric power networks

Hierarchical fair competition algorithm for optimization of reliability of complex electric power networks

Design of Polynomial Fuzzy Radial Basis Function Neural Networks Based on Nonsymmetric Fuzzy Clustering and Parallel Optimization

We first propose a Parallel Space Search Algorithm (PSSA) and then introduce a design of Polynomial Fuzzy Radial Basis Function Neural Networks (PFRBFNN) based on Nonsymmetric Fuzzy Clustering Method (NSFCM) and PSSA. The PSSA is a parallel optimization algorithm realized by using Hierarchical Fair Competition strategy. NSFCM is essentially an improved fuzzy clustering method, and the good performance in the design of “conventional” Radial Basis Function Neural Networks (RBFNN) has been proven. In the design of PFRBFNN, NSFCM is used to design the premise part of PFRBFNN, while the consequence part is realized by means of weighted least square (WLS) method. Furthermore, HFC-PSSA is exploited here to optimize the proposed neural network. Experimental results demonstrate that the proposed neural network leads to better performance in comparison to some existing neurofuzzy models encountered in the literature.

Hydrofoil optimization and experimental validation in helical vertical axis turbine for power generation from marine current

A kind of hydrofoil, which is especially for use by helical vertical axis turbine for power generation from marine current, has been optimized by means of traditional genetic algorithms combined with hierarchical fair competition model. Bezier curve is used to parameterize the hydrofoils and numerical analysis is also introduced to evaluate hydrodynamic performance. Special fitness function as well as modified total lift–drag ratio is discussed in detail. Turbine performance tests and flow visualization experiments by PIV are conducted in order to draw a comparison between the optimized and two other competitive hydrofoils. The experimental results demonstrate that the optimized is more suitable to marine condition and beneficial to turbine efficiency improvement, and can be expected to act as a prospective hydrofoil for helical vertical axis turbine.

An integrated blade optimization approach based on parallel ANN and GA with hierarchical fair competition dynamic-niche

This work presented an available multi-point blade optimization procedure for better aerodynamic performances. Based on the proposed Parallel ANN and GA with hierarchical fair competition dynamic-niche (GA-HFCDN), an integrated approach for the blade optimization design was put forward by combining Bezier parameterization with FINE/TURBO solver. In the optimization design, parallel ANN was employed to build a more proper approximate model. GA-HFCDN was proposed to obtain the global optimization solution more efficiently for the blade design. Two research cases, including plane cascades blade optimization and the optimization and experimental study of a low specific speed centrifugal blower blade, were performed by using the above approach. The conclusions showed the rationality and validity of the optimization approach.

Blade optimization design and performance investigations of an ultra-low specific speed centrifugal blower

The ultra-low specific speed centrifugal blower is widely used in energy industries due to its features such as low flow rate, high pressure and low manufacturing cost. However, the width-to-diameter ratio of the above blower becomes relatively small to satisfy the needed operation condition and its performances are considerably degraded as a result of relatively high leakage, disc friction and passage friction loss consequently. The purpose of this paper is to improve its performance through the optimization design of the blade’s profile properly. Based on artificial neural networks (ANN) and hierarchical fair competition genetic algorithms with dynamic niche (HFCDN-GAs), the optimization design approach is established. By conjoining Bezier parameterization and FINE/TURBO solver, the optimized blade is designed by adjusting the profile gradually. An industrial ultra-low specific speed centrifugal blower with parallel hub and shroud has been selected as a reference case for optimization design. The performance investigations of the centrifugal blowers with different types of blades are conducted. The conclusions of the performance improvement of the optimized blade provide positive evidences in the application of the optimization design of the above blower blade.

Application design of sustainable simulated algorithm based on hierarchical fair competition model

A kind of medical experiment arrangement arrangement problem,which was a constrained optimization was solved by using an improved Simulated Annealing algorithm based on Hierarchical Fair Competition Model(HFCSA) through modifying the definition of individual,designing new coding method and growth pattern,and proposing efficient operators.The results show that the time expense of the new optimal solution is 77% of the older one.The success to solve medical experiment arrangement problem has widened the HFCSA's application field and found out a new way for solving some similar constrained optimization problems.

Efficient hybrid evolutionary optimization of interatomic potential models

The lack of adequately predictive atomistic empirical models precludes meaningful simulations for many materials systems. We describe advances in the development of a hybrid, population based optimization strategy intended for the automated development of material specific interatomic potentials. We compare two strategies for parallel genetic programming and show that the Hierarchical Fair Competition algorithm produces better results in terms of transferability, despite a lower training set accuracy. We evaluate the use of hybrid local search and several fitness models using system energies and/or particle forces. We demonstrate a drastic reduction in the computation time with the use of a correlation-based fitness statistic. We show that the problem difficulty increases with the number of atoms present in the systems used for model development and demonstrate that vectorization can help to address this issue. Finally, we show that with the use of this method, we are able to "rediscover" the exact model for simple known two- and three-body interatomic potentials using only the system energies and particle forces from the supplied atomic configurations.

An investigation on niching multiple species based on population replacement strategies for multimodal functions optimization

This paper studies the niching mechanism based on population replacement in the process of evolution to solve the multimodal functions optimization (MMFO) problems. In order to niche multiple species for the MMFO tasks, the overlapping population replacement is surely needed because the offspring population most probably does not inherit all of the genetic information contained in its parental population, and the basic procedure for niching genetic algorithms with overlapping population replacement is presented. Then four niching schemes, the nearest neighbors replacement crowding (NNRC), the species conservation technique (SCT), the HFC-I (implicit hierarchical fair competition), and the CPE (clearing procedure with elitist) are investigated. These niching schemes are characterized with regard to different niching strategies and parameterizations, and the corresponding niching procedures are outlined. Finally, experiments are carried out on a suite of test functions to compare different niching strategies regarding niching efficiency and scalability. Experimental results illustrate the intrinsic difference of the four niching schemes. The NNRC and HFC-I have a mechanism of multiple species coevolution via adapting multiple species to different niches, while the SCT and CPE tend to make use of a mandatory mechanism to conserve species just like the grid searching over the solution space based on species distance or clearing radius. All niching methods are able to deal with complex MMFO problems, while the NNRC and HFC-I show a better performance in terms of niching efficiency and scalability, and are more robust regarding the algorithm parameterization.

Population Diversity and Algorithm Realization of Sustainable HFC Model

HFC (Hierarchical Fair Competition) model is a kind of sustainable EA based on hierarchical search. On the basis of studying the sustainable evolutionary theory of HFC model deeply, in the HFC model the admission and export threshold are proposed to divide all fitness grade. And it is suggested that both static and active thresholds is used by the complication of different problems. In addition, through analyzing the parameters and topology space of HFC model, it is gained to the organize framework and detailed algorithm structure. Finally, it is proved by exercise that HFC model can maintain the diversity of population and assure the fair competition during individuals effectively.

Aerodynamic optimization for turbine blade based on hierarchical fair competition genetic algorithms with dynamic niche

A global optimization approach to turbine blade design based on hierarchical fair competition genetic algorithms with dynamic niche (HFCDN-GAs) coupled with Reynolds-averaged Navier-Stokes (RANS) equation is presented.In order to meet the search theory of GAs and the aerodynamic performances of turbine,Bezier curve is adopted to parameterize the turbine blade profile,and a fitness function pertaining to optimization is designed.The design variables are the control points' ordinates of characteristic polygon of Bezier curve representing the turbine blade profile.The object function is the maximum lift-drag ratio of the turbine blade.The constraint conditions take into account the leading and trailing edge metal angle,and the strength and aerodynamic performances of turbine blade.And the treatment method of the constraint conditions is the flexible penalty function.The convergence history of test function indicates that HFCDN-GAs can locate the global optimum within a few search steps and have high robustness.The lift-drag ratio of the optimized blade is 8.3% higher than that of the original one.The results show that the proposed global optimization approach is effective for turbine blade.

Phase compensating dielectric lens design with genetic programming

This article illustrates a microwave dielectric lens design using genetic programming (GP), which, to the best knowledge of the authors, is the first time GP has been applied to design a microwave dielectric lens. A phase-compensating single layer lens and a quarter-wave phase-compensating multilayer dielectric lens, which uses four types of materials and a zoned structure, was designed using GP. The dielectric lens shape was designed using GP strategy, with a random initial shape, to compensate for the phase error of the multilayer dielectric lens and to achieve the required performance. Standard GP (SGP) and hierarchical fair competition genetic programming (HFC-GP) was applied to obtain the solution. The results show that this novel method of microwave lens shape design using GP is both accurate and stable. In addition, the simulation results of using high frequency simulation software (HFSS) are illustrated and compared. © 2007 Wiley Periodicals, Inc. Int J RF and Microwave CAE, 2007.

Tribe-PSO: A novel global optimization algorithm and its application in molecular docking

One of the goals of researches on molecular docking is to develop robust algorithms with powerful ability in global optimization because the primary purpose of molecular docking is to find out the most stable conformation of receptor–ligand complexes. Inspired by the principles of Hierarchical Fair Competition, we propose Tribe-PSO, a multi-layered and multi-phased hybrid particle swarm optimization model, in this article. With benchmarks on several widely used multimodal test functions, Tribe-PSO proved to be a powerful global optimization algorithm. Tribe-PSO is then incorporated with the scoring functions in AutoDock 3.05 to perform molecular docking simulations of 100 receptor–ligand complexes from the Protein Data Bank. The simulation results show that the docking procedures using Tribe-PSO method generally find out the stable conformation as well as the native-like complexes more efficiently and accurately than GA–LS combination which is used in AutoDock 3.05 as the optimizer. All the tests demonstrate that Tribe-PSO is a promising search algorithm for molecular docking problems.

The Hierarchical Fair Competition (HFC) Framework for Sustainable Evolutionary Algorithms

Many current Evolutionary Algorithms (EAs) suffer from a tendency to converge prematurely or stagnate without progress for complex problems. This may be due to the loss of or failure to discover certain valuable genetic material or the loss of the capability to discover new genetic material before convergence has limited the algorithm's ability to search widely. In this paper, the Hierarchical Fair Competition (HFC) model, including several variants, is proposed as a generic framework for sustainable evolutionary search by transforming the convergent nature of the current EA framework into a non-convergent search process. That is, the structure of HFC does not allow the convergence of the population to the vicinity of any set of optimal or locally optimal solutions. The sustainable search capability of HFC is achieved by ensuring a continuous supply and the incorporation of genetic material in a hierarchical manner, and by culturing and maintaining, but continually renewing, populations of individuals of intermediate fitness levels. HFC employs an assembly-line structure in which subpopulations are hierarchically organized into different fitness levels, reducing the selection pressure within each subpopulation while maintaining the global selection pressure to help ensure the exploitation of the good genetic material found. Three EAs based on the HFC principle are tested - two on the even-10-parity genetic programming benchmark problem and a real-world analog circuit synthesis problem, and another on the HIFF genetic algorithm (GA) benchmark problem. The significant gain in robustness, scalability and efficiency by HFC, with little additional computing effort, and its tolerance of small population sizes, demonstrates its effectiveness on these problems and shows promise of its potential for improving other existing EAs for difficult problems. A paradigm shift from that of most EAs is proposed: rather than trying to escape from local optima or delay convergence at a local optimum, HFC allows the emergence of new optima continually in a bottom-up manner, maintaining low local selection pressure at all fitness levels, while fostering exploitation of high-fitness individuals through promotion to higher levels.

A novel evolutionary engineering design approach for mixed-domain systems

This paper presents an approach to engineering design of mixed-domain dynamic systems. The approach aims at system-level design and has two key features: first, it generates engineering designs that satisfy predefined specifications in an automatic manner; second, it can design systems belonging to different or mixed physical domains, such as electrical, mechanical, hydraulic, pneumatic, thermal systems and/or a mixture of them. Two important tools are used in this approach, namely, bond graphs and genetic programming. Bond graphs are useful because they are domain independent, amenable to free structural composition, and are efficient for classification and analysis, allowing rapid determination of various types of acceptability or feasibility of candidate designs. Genetic programming, on the other hand, is a powerful tool for open-ended topological search. To prevent the premature convergence often encountered in evolutionary computation, a hierarchical fair competition model is adopted in this work. Examples of an analog filter design and an MEM filter design illustrate the application of the approach.