MAGAD-BFS: A learning method for Beta fuzzy systems based on a multi-agent genetic algorithm

This paper proposes a learning method for Beta fuzzy systems (BFS) based on a multiagent genetic algorithm. This method, called Multi-Agent Genetic Algorithm for the Design of BFS has two advantages. First, thanks to genetic algorithms (GA) efficiency, it allows to design a suitable and precise model for BFS. Second, it improves the GA convergence by reducing rule complexity thanks to the distributed implementation by multi-agent approach. Dynamic agents interact to provide an optimal solution in order to obtain the best BFS reaching the balance interpretability-precision. The performance of the method is tested on a simulated example.     

Adaptive molecular docking method based on information entropy genetic algorithm

Almost all the molecule docking models, using by widespread docking software, are approximate. Approximation will make the scoring function inaccurate under some circumstances. This study proposed a new molecule docking scoring method: based on force-field scoring function, it use information entropy genetic algorithm to solve the docking problem. Empirical-based and knowledge-based scoring function are also considered in this method. Instead of simple combination with fixed weights, coefficients of each factor are adaptive in the process of searching optimum solution. Genetic algorithm with the multi-population evolution and entropy-based searching technique with narrowing down space is used to solve the optimization model for molecular docking problem. To evaluate this method, we carried out a numerical experiment with 134 protein–ligand complexes of the publicly available GOLD test set. The results show that this study improved the docking accuracy over the individual force-field scoring greatly. Comparing with other popular docking software, it has the best average Root-Mean-Square Deviation (RMSD). The average computing time of this study is also good among them.     

An application of genetic algorithm methods for teacher assignment problems

The arrangement of courses at universities is an optimal problem to be discussed under multiple constraints. It can be divided into two parts: teacher assignments and class scheduling. This paper focused primarily on teacher assignments. Consideration was given to teacher's professional knowledge, teacher preferences, fairness of teaching overtime, school resources, and the uniqueness of the school's management. Traditional linear programming methods do not obtain satisfactory results with this complex problem.In this paper, genetic algorithm methods were used to deal with the issue of multiple constraints. As a global optimal searching method, the results of this study indicated that genetic algorithms can save significant time spent on teacher assignments and are more acceptable by the teachers.     

Hybrid real coded genetic algorithm solution to economic dispatch problem

This paper presents a new, two-phase hybrid real coded genetic algorithm (GA) based technique to solve economic dispatch (ED) problem with multiple fuel options. The proposed hybrid scheme is developed in such a way that a simple real coded GA is acting as a base level search, which makes a quick decision to direct the search towards the optimal region, and local optimization by direct search and systematic reduction in size of the search region method is next employed to do the fine tuning. Constraint satisfaction technique has been employed to improve the solution quality and reduce the computational expenses. In order to validate the effectiveness of the proposed hybrid real coded genetic algorithm, the result of 10-generation unit ED problem with multiple fuel options is considered. The result shows that the proposed hybrid algorithm not only improves the solution accuracy and reliability but also makes the algorithm more efficient in terms of number of function evaluations and computation time. The simulation study clearly demonstrates that the proposed hybrid real coded genetic algorithm is practical and valid for real-time applications.     

Hybrid genetic algorithm for the identification of high-order synchronous machine two-axis equivalent circuits

A hybrid genetic algorithm is used to find high-order equivalent circuits (ECs) of synchronous machines using standstill frequency response (SSFR) data. The algorithm performs satisfactorily despite the great deal of local minima surrounding the optimal solution of high-order ECs. It gives circuit parameters that simultaneously fit the three independent transfer functions given by the d-axis two-port network of the synchronous machine. It is found that as the order of the EC is increased, the optimization index used in the identification procedure is enhanced in a clear fashion. This leads to a new way for determining the right number of rotor branches required to correctly reproduce the SSFR data. The q-axis network is also analyzed with the hybrid algorithm. The so-called Canay's inductances are included in this one-port network to test if the fitting properties of the q-axis EC can be improved. The SSFR data used in this work is generated by a finite element model of a turbine generator, but actual data can also be readily handled.     

A genetic algorithm based approach to the mixed-model assembly line balancing problem of type II

Mixed-model assembly lines allow for the simultaneous assembly of a set of similar models of a product, which may be launched in the assembly line in any order and mix. As current markets are characterized by a growing trend for higher product variability, mixed-model assembly lines are preferred over the traditional single-model assembly lines.

This paper presents a mathematical programming model and an iterative genetic algorithm-based procedure for the mixed-model assembly line balancing problem (MALBP) with parallel workstations, in which the goal is to maximise the production rate of the line for a pre-determined number of operators.

The addressed problem accounts for some relevant issues that reflect the operating conditions of real-world assembly lines, like zoning constraints and workload balancing and also allows the decision maker to control the generation of parallel workstations.     

A variable-grouping based genetic algorithm for large-scale integer programming

To effectively reduce the dimensionality of search space, this paper proposes a variable-grouping based genetic algorithm (VGGA) for large-scale integer programming problems (IPs). The VGGA first groups IP’s decision variables based on the optimal solution to the IP’s continuous relaxation problem, and then applies a standard genetic algorithm (GA) to the subproblem for each group of variables. We compare the VGGA with the standard GA and GAs based on even variable-grouping by applying them to solve randomly generated convex quadratic knapsack problems and integer knapsack problems. Numerical results suggest that the VGGA is superior to the standard GA and GAs based on even variable-grouping both on computation time and solution quality.     

A surrogate-assisted evolutionary algorithm based on the genetic diversity objective

In this work, a novel surrogate-assisted memetic algorithm is proposed which is based on the preservation of genetic diversity within the population. The aim of the algorithm is to solve multi-objective optimization problems featuring computationally expensive fitness functions in an efficient manner. The main novelty is the use of an evolutionary algorithm as global searcher that treats the genetic diversity as an objective during the evolution and uses it, together with a non-dominated sorting approach, to assign the ranks. This algorithm, coupled with a gradient-based algorithm as local searcher and a back-propagation neural network as global surrogate model, demonstrates to provide a reliable and effective balance between exploration and exploitation. A detailed performance analysis has been conducted on five commonly used multi-objective problems, each one involving distinct features that can make the convergence difficult toward the Pareto-optimal front. In most cases, the proposed algorithm outperformed the other state-of-the-art evolutionary algorithms considered in the comparison, assuring higher repeatability on the final non-dominated set, deeper convergence level and higher convergence rate. It also demonstrates a clear ability to widely cover the Pareto-optimal front with larger percentage of non-dominated solutions if compared to the total number of function evaluations.     

Design of steel roofs subjected to drifted snow using genetic optimization

Formed metal deck roofs play an important role in steel building construction. There are many design variables in deck design to make it important for designers to put the right combination together to achieve a better design. In this paper, a procedure for the design of steel roofs subjected to non-uniform loads such as drifted snow is presented. The presented approach uses genetic algorithms to achieve optimal or near-optimal designs. The advantages of the model stem from two main points: (1) it searches for the optimum or near-optimum number of purlins for the roof; and (2) it determines the near-optimum spacing between purlins to minimize the structure weight. Details of model development are described and example applications are presented to demonstrate the model capabilities.     

A hybrid genetic algorithm based heuristic for an integrated supply chain problem

Supply chain management is becoming an essential component of efficient decision-making for companies, as they are increasingly implementing optimization strategies in order to improve their business performance. In this paper we develop a capacitated multi-echelon joint location-inventory model, according to which a single product is distributed from a manufacturer to retailers through a set of warehouses, the locations of which are to be determined by the model. Each retailer is assigned exactly one warehouse, while each warehouse can serve multiple retailers. The model decides where to locate warehouses, assigns retailers to the warehouses and decides the times between orders at the warehouses and the retailers, so as to minimize the cost of operating the supply chain. Notably, the model considers capacity constraints for each warehouse, ensuring that the reorder quantity is below the capacity limit. We develop a genetic algorithm (GA) based heuristic to solve the problem and the GA is validated on small size problems by comparing its solution to the optimal solution obtained by the General Algebraic Modeling System (GAMS). We focus our attention on specifically customizing the GA and thus an experimental analysis is carried out to find the optimal parameter setting for the GA as well as to obtain insights on the effect of the various GA parameters. Finally, a sensitivity analysis is conducted to show the effect of the capacity constraints.