Variable Genetic Algorithm Research Articles

A novel strategy for the synthesis of thinned planar antenna array which furnishes lowest possible peak side lobe level without appearance of grating lobes over wide steering angles

In this paper, we propose a novel strategy for the synthesis of uniformly excited thinned planar antenna (UE-TPA) arrays which renders maximum possible reduction in the peak side lobe level (PSLL) with minimum number of turn ‘ON’ elements over wide steering angles. The main objective of this paper is to investigate an efficient variant of genetic algorithm (GA) which yields good quality solution at affordable computational complexity. Towards this aim, authors attempted to develop a powerful synthesis tool denoted as improved binary coded GA (IBc-GA) by investigating novel chromosome arrangements and ameliorated process techniques for crossover and mutation operations to improve the flexibility as well as the global search ability in achieving the best possible reduction in PSLL without increasing computational burden. In order to corroborate the effectiveness and superiority of the proposed synthesis technique, two examples consisting of 10 × 10- and 14 × 14-elements UE-TPA arrays are numerically examined at antenna boresight as well as when the arrays are electronically steered up to 40° with respect to antenna boresight in both azimuth and elevation planes. The numerical results of 10 × 10-element UE-TPA obtained through the proposed synthesis method at antenna boresight are compared with those determined at 20° and 40° scan angles and with those of same size arrays reported in the literature. In addition, results at antenna boresight achieved by proposed method for the case of 14 × 14-element UE-TPA array are compared with those determined at 20° and 40° scan angles. It is inferred from the investigation that wide angle scanning with lowest possible PSLL and without appearance of grating lobes is achieved at comparable computational cost.

Evolutionary algorithms for solving the airline crew pairing problem

Abstract Solving the airline crew pairing problem (CPP) requires a search to generate a set of minimum-cost crew pairings covering all flight legs, subject to a set of constraints. We propose a solution comprising two consecutive stages: crew pairing generation, followed by an optimisation stage. First, all legal crew pairings are generated with the given flights, and then the best subset of those pairings with minimal cost are chosen via an optimisation, process based on an evolutionary algorithm. This paper investigates the performance of two previously proposed genetic algorithm (GA) variants, and a memetic algorithm (MA) hybridising GA with hill climbing, for solving the CPP. The empirical results across a set of benchmark real-world instances illustrate that the proposed MA is the best performing approach overall.

A biased random key genetic algorithm applied to the electric distribution network reconfiguration problem

This work presents a biased random-key genetic algorithm (BRKGA) to solve the electric distribution network reconfiguration problem (DNR). The DNR is one of the most studied combinatorial optimization problems in power system analysis. Given a set of switches of an electric network that can be opened or closed, the objective is to select the best configuration of the switches to optimize a given network objective while at the same time satisfying a set of operational constraints. The good performance of BRKGAs on many combinatorial optimization problems and the fact that it has never been applied to solve DNR problems are the main motivation for this research. A BRKGA is a variant of random-key genetic algorithms, where one of the parents used for mating is biased to be of higher fitness than the other parent. Solutions are encoded by using random keys, which are represented as vectors of real numbers in the interval (0,1), thus enabling an indirect search of the solution inside a proprietary search space. The genetic operators do not need to be modified to generate only feasible solutions, which is an exclusive task of the decoder of the problem. Tests were performed on standard distribution systems used in DNR studies found in the technical literature and the performance and robustness of the BRKGA were compared with other GA implementations.

NEW GENERATION NETWORKS STRUCTURAL SYNTHESIS WITH APPLICATION OF MODIFIED GENETIC ALGORITHM

Background. The problem of new generation computer network structural synthesis is considered under constraints on Quality of Service (QoS) – average packets transfer delay (PTD) and packets loss ratio (PLR). Besides network structure it is necessary to determine simultaneously channels capacities and find flow distribution for all classes of service (customers). This problem refers to so-called NP-difficult combinatorial optimization problems for which there are no exact algorithms of polynomial computational complexity. Therefore in the paper modified genetic algorithm (GA) is suggested for its solution. Objective . The aim of the paper is to investigate different modifications (variants) of genetic algorithms: deterministic, adaptive and self-adjusted and estimate their efficiency for the solution of the considered problem. Methods. Based on analysis of literature on GA classes of algorithms were detected differed by variants of strategic parameters adaptation - probabilities of crossover and mutations. Different combinations of adaptation methods were implemented and analyzed – deterministic, adaptive and self- adjusting. Results. The following variants of GA were investigated in the research: • the combination of unconditional crossover and dynamic deterministic mutation; • the combination of unconditional crossover and dynamic adaptive mutation; • the combination of unconditional crossover and dynamic self-adjusting mutation; • the combination dynamic deterministic crossover and dynamic mutation; • the combination dynamic adaptive crossover an unconditional mutation. Conclusions. In result of investigations it was established that the most efficient for the computer networks structural synthesis is the genetic algorithm with combination of dynamic adaptive crossover an unconditional mutation. This algorithm was implemented in software kit and applied for real problem of NG network structural design and its efficiency was estimated.

An improved class of real-coded Genetic Algorithms for numerical optimization✰

Over the last few decades, many improved Evolutionary Algorithms (EAs) have been proposed to tackle different types of optimization problems. Genetic Algorithm (GA) among other canonical algorithms have not shown consistent performance over a range of different optimization problems with complex characteristics. In this paper, an improved class of real-coded Genetic Algorithm is introduced to solve complex optimization problems. The first algorithm, Genetic Algorithm embedded with a new Differential Evolution crossover, GA–DEx, proposes a new variant of Differential Evolution mutation which is used as a new multi-parent crossover in Genetic Algorithms. The main purpose of this algorithm is to enhance the search ability of the GA algorithm by combining a new Differential Evolution crossover with a GA algorithm to avoid premature convergence and stagnation scenarios by exploring more solutions in the problem search space. The second amalgam algorithm, GA–DExSPS, uses an effective and efficient successful parent selection strategy to provide a successful alternative for the selection of parents during the Differential Evolution crossover process. This strategy improves the performance of first introduced algorithm by selecting more promising parents to guide the evolutionary search. The third algorithm, GA–aDExSPS, introduces an aging mechanism and a success-history-based adaptive Genetic Algorithm. This algorithm adapts the alpha parameter used by Differential Evolution crossover in a history-based adaptive manner. This adaptation helps the search to discover more promising regions and to prevent stagnation and premature convergence scenarios. To verify the performance of our class, a challenging test suite of 30 benchmark functions from the IEEE CEC2014 real parameter single objective competition is used. The results affirm the effectiveness and robustness of the proposed algorithms compared to other state-of-the-art well-known crossovers and recent Genetic Algorithms variants.

Multi-objective optimal design of hybrid viscoelastic/composite sandwich beams by using the generalized differential quadrature method and the non-dominated sorting genetic algorithm II

In this study, the multi-objective optimal design of hybrid viscoelastic/composite sandwich beams for minimum weight and minimum vibration response is aimed. The equation of motion for linear vibrations of a multi-layer beam is derived by using the principle of virtual work in the most general form. These governing equations together with the boundary conditions are discretized by the generalized differential quadrature method (GDQM) in the frequency domain for the first time. Also, the time and temperature dependent properties of the viscoelastic materials are taken into consideration by a novel ten-parameter fractional derivative model that can realistically capture the response of these materials. The material variability is accounted for by letting an optimization algorithm choose a material freely out of four fiber-reinforced composite materials and five viscoelastic damping polymers for each layer. The design parameters, i.e., the orientation angles of the composites, layer thicknesses and the layer materials that give the set of optimal solutions, namely the Pareto frontier, is obtained for the three and nine-layered clamped-free sandwich beams by using a variant of the non-dominated sorting genetic algorithms (NSGA II).

Bio-inspired heuristics hybrid with sequential quadratic programming and interior-point methods for reliable treatment of economic load dispatch problem

In the present study, bio-inspired computational heuristics are exploited for finding the solution of economic load dispatch (ELD) problem with valve point loading effect using variants of genetic algorithm (GA) hybrid with sequential quadratic programming (SQP) and interior-point algorithms (IPAs). Variants of GAs are constructed using different sets of routines for its fundamental operators in order to explore the entire search space for global optimum solutions while SQP and IPA are integrated with GAs for rapid local convergence. Nine variants of each design scheme based on GAs, GA-SQP and GA-IPAs are applied on three different ELD problems of thermal power plant systems. Comparative studies of the proposed schemes are performed through the results of statistical performance indices in order to establish the worth and effectiveness in terms of accuracy, convergence and complexity measures.

Design of Bio-inspired Heuristic Techniques Hybridized with Sequential Quadratic Programming for Joint Parameters Estimation of Electromagnetic Plane Waves

In this study, intelligent hybrid computing techniques are developed using variants of genetic algorithms (GAs) to estimate jointly direction of arrival and amplitude of electromagnetic plane waves. Fitness evaluation function is formulated for parameter estimation model by exploiting the approximation theory in mean square sense based on error between the desired and estimated responses. Optimization of design variables of the model is carried out with hybrid schemes through variant of GAs integrated with sequential quadratic programming for rapid refinement. Proposed schemes are applied to number of electromagnetic plane waves impinging on uniform linear array from different directions with different amplitudes. Comparison of the results is done with true parameters of the system in order to evaluate the performance of the algorithms. Monte-Carlo simulations for the design approaches are carried out to analyze their strength in terms of estimation accuracy, robustness against noise, convergence and proximity effects.

A variant of genetic algorithm for non-homogeneous population

Selection of initial points, the number of clusters and finding proper clusters centers are still the main challenge in clustering processes. In this paper, we suggest genetic algorithm based method which searches several solution spaces simultaneously. The solution spaces are population groups consisting of elements with similar structure. Elements in a group have the same size, while elements in different groups are of different sizes. The proposed algorithm processes the population in groups of chromosomes with one gene, two genes to k genes. These genes hold corresponding information about the cluster centers. In the proposed method, the crossover and mutation operators can accept parents with different sizes; this can lead to versatility in population and information transfer among sub-populations. We implemented the proposed method and evaluated its performance against some random datasets and the Ruspini dataset as well. The experimental results show that the proposed method could effectively determine the appropriate number of clusters and recognize their centers. Overall this research implies that using heterogeneous population in the genetic algorithm can lead to better results.

Novel search space updating heuristics-based genetic algorithm for optimizing medium-scale airline crew pairing problems

This study examines the crew pairing problem, which is one of the most comprehensive problems encountered in airline planning, to generate a set of crew pairings that has minimal cost, covers all flight legs and fulfils legal criteria. In addition, this study examines current research related to crew pairing optimization. The contribution of this study is developing heuristics based on an improved dynamic-based genetic algorithm, a deadhead-minimizing pairing search and a partial solution approach (less-costly alternative pairing search). This study proposes genetic algorithm variants and a memetic algorithm approach. In addition, computational results based on real-world data from a local airline company in Turkey are presented. The results demonstrate that the proposed approach can successfully handle medium sets of crew pairings and generate higher-quality solutions than previous methods.

On the applicability of Diploid Genetic Algorithms in Vertex Cover Problem

Diploid Genetic Algorithm is a variant of Genetic Algorithms and is based on the concept of diploidty. The algorithm ensures robustness and hence has the capacity of producing better results as compared to Simple Genetic Algorithms. This work presents a novel approach to solve the Vertex Cover Problem using Diploid Genetic Algorithms. The work is verified using DIMACS and has been compared with Simple Genetic Algorithms. Utmost care has been taken to choose the samples and design the experimental setup. The results have been reported and analyzed. The results are encouraging. The paper also presents the future scope and the fundamentals. The work paves way of Diploid Genetic Algorithms in Vertex Cover and similar problems.

Memetic computing through bio-inspired heuristics integration with sequential quadratic programming for nonlinear systems arising in different physical models.

BackgroundIn this study, bio-inspired computing is exploited for solving system of nonlinear equations using variants of genetic algorithms (GAs) as a tool for global search method hybrid with sequential quadratic programming (SQP) for efficient local search. The fitness function is constructed by defining the error function for systems of nonlinear equations in mean square sense. The design parameters of mathematical models are trained by exploiting the competency of GAs and refinement are carried out by viable SQP algorithm.ResultsTwelve versions of the memetic approach GA-SQP are designed by taking a different set of reproduction routines in the optimization process. Performance of proposed variants is evaluated on six numerical problems comprising of system of nonlinear equations arising in the interval arithmetic benchmark model, kinematics, neurophysiology, combustion and chemical equilibrium. Comparative studies of the proposed results in terms of accuracy, convergence and complexity are performed with the help of statistical performance indices to establish the worth of the schemes.ConclusionsAccuracy and convergence of the memetic computing GA-SQP is found better in each case of the simulation study and effectiveness of the scheme is further established through results of statistics based on different performance indices for accuracy and complexity.

Newton’s Law of Universal Gravitation Based Genetic Algorithm for Graph Distribution

This paper proposes a novel versatile genetic algorithm (GA) for solving the graph distribution problem. The new GA is based mainly on an inspirational idea that exploits the Newton’s universal gravitation law to introduce a novel GA fitness function. By this,the new GA preserves the workload balancing property between the different sites of the graph network and reduces the inter-processors communications overhead. Moreover, three main variants of the novel GA are developed. The two first; centralized and distributed variants, are developed to conduct a graph distribution over homogeneous architectures. The third variant is a distributed one devoted for heterogeneous architectures where the impact of the auto-adaptation features of the GA emerges. The results obtained and the comparative studies show the effectiveness of the proposed methods.

Mapping stream programs onto multicore platforms by local search and genetic algorithm

This paper presents a number of novel metaheuristic approaches that can efficiently map stream graphs on multicores. A stream graph consists of a set of actors performing different functions communicating through edges. Orchestrating stream graphs on multicores can be formulated as an Integer Linear Programming (ILP) problem but ILP solver takes exponential time to provide an optimal solution. We propose metaheuristic algorithms to achieve near optimal solutions within a reasonable amount of time. We employ six different variants of the Hill-Climbing (HC) algorithm employing different tweak operators that produce excellent result extremely quickly. We also propose six different variants of Genetic Algorithm (GA) to examine how effective these variants can be in escaping the local optima. We finally combine HC and GA techniques (which is also known as ‘memetic algorithm’) to produce hybrid techniques that outperform the individual performance of HC and GA techniques. We compare our results with the results generated by the CPLEX optimization tool. Our best technique has achieved a geometric mean speedup of 7.42× across a range of StreamIt benchmarks on an eight-core processor.

A hybrid of clustering and quantum genetic algorithm for relevant genes selection for cancer microarray data

In order to efficiently explore and exploit large search space, quantum variant of genetic algorithm has been suggested in literature. It utilizes quantum computing principle and genetic operators. Despite the use of the quantum variant of GA, memory and computation time requirements for high dimensional data like microarrays are huge. In this paper, we propose a hybrid approach, ClusterQGA, that uses clustering to select a small set of non-redundant representative genes and then applies Quantum Genetic Algorithm to determine a minimal set of relevant and non-redundant genes. Also a new fitness function is proposed to reduce number of genes without sacrificing the classification accuracy. The effectiveness of the proposed approach in comparison to existing methods in terms of classification accuracy and number of features has been experimentally established for both binary and multi-class publicly available cancer microarray datasets. The proposed approach reduces the computation time of Quantum Genetic Algorithm for high dimension microarray data.

Reservoir optimization with differential evolution

Reservoir optimization, is one of recent problems, which has been researched by several methods such as Linear Programming (LP), Non-linear Programming (NLP), Genetic Algorithm (GA), and Dynamic Programming (DP). Differential Evolution (DE), a method in GA group, is recently applied in many fields, especially water management. This method is an improved variant of GA to converge and reach to the optimal solution faster than the traditional GA. It is also capable to apply for a wide range space, to a problem with complex, discontinuous, undifferential optimal function. Furthermore, this method does not requirethe gradient information of the space but easily find the global solution by asimple algorithm. In this paper, we introduce DE, compare to LP which was considered mathematically decades ago to prove DE's accuracy, then apply DE to Pleikrong, a reservoir in Vietnam, then discuss about the results.

Feature Selection via Chaotic Antlion Optimization.

BackgroundSelecting a subset of relevant properties from a large set of features that describe a dataset is a challenging machine learning task. In biology, for instance, the advances in the available technologies enable the generation of a very large number of biomarkers that describe the data. Choosing the more informative markers along with performing a high-accuracy classification over the data can be a daunting task, particularly if the data are high dimensional. An often adopted approach is to formulate the feature selection problem as a biobjective optimization problem, with the aim of maximizing the performance of the data analysis model (the quality of the data training fitting) while minimizing the number of features used.ResultsWe propose an optimization approach for the feature selection problem that considers a “chaotic” version of the antlion optimizer method, a nature-inspired algorithm that mimics the hunting mechanism of antlions in nature. The balance between exploration of the search space and exploitation of the best solutions is a challenge in multi-objective optimization. The exploration/exploitation rate is controlled by the parameter I that limits the random walk range of the ants/prey. This variable is increased iteratively in a quasi-linear manner to decrease the exploration rate as the optimization progresses. The quasi-linear decrease in the variable I may lead to immature convergence in some cases and trapping in local minima in other cases. The chaotic system proposed here attempts to improve the tradeoff between exploration and exploitation. The methodology is evaluated using different chaotic maps on a number of feature selection datasets. To ensure generality, we used ten biological datasets, but we also used other types of data from various sources. The results are compared with the particle swarm optimizer and with genetic algorithm variants for feature selection using a set of quality metrics.

A Variant of Genetic Algorithm Based Categorical Data Clustering for Compact Clusters and an Experimental Study on Soybean Data for Local and Global Optimal Solutions

Almost all partitioning clustering algorithms getting stuck to the local optimal solutions. Using Genetic algorithms (GA) the results can be find globally optimal. This piece of work offers and investigates a new variant of the Genetic algorithm (GA) based k-Modes clustering algorithm for categorical data. A statistical analysis have been done on the popular categorical dataset which shows the user specified cluster centres stuck at local optimal solution in K-modes algorithm even in all the higher iterations and the proposed algorithm overcome this problem of local optima. To the best of our knowledge, such comparison has been reported here for the first time for the case of categorical data. The obtained results, shows that the proposed algorithm is better over the conventional k-Modes algorithm in terms of optimal solutions and within cluster variation measure.

Evolutionary approaches to signal decomposition in an application service management system

The increased demand for autonomous control in enterprise information systems has generated interest on efficient global search methods for multivariate datasets in order to search for original elements in time-series patterns, and build causal models of systems interactions, utilization dependencies, and performance characteristics. In this context, activity signals deconvolution is a necessary step to achieve effective adaptive control in Application Service Management. The paper investigates the potential of population-based metaheuristic algorithms, particularly variants of particle swarm, genetic algorithms and differential evolution methods, for activity signals deconvolution when the application performance model is unknown a priori. In our approach, the Application Service Management System is treated as a black- or grey-box, and the activity signals deconvolution is formulated as a search problem, decomposing time-series that outline relations between action signals and utilization-execution time of resources. Experiments are conducted using a queue-based computing system model as a test-bed under different load conditions and search configurations. Special attention was put on high-dimensional scenarios, testing effectiveness for large-scale multivariate data analyses that can obtain a near-optimal signal decomposition solution in a short time. The experimental results reveal benefits, qualities and drawbacks of the various metaheuristic strategies selected for a given signal deconvolution problem, and confirm the potential of evolutionary-type search to effectively explore the search space even in high-dimensional cases. The approach and the algorithms investigated can be useful in support of human administrators, or in enhancing the effectiveness of feature extraction schemes that feed decision blocks of autonomous controllers.

Genetic Algorithm for Integrated Models of Continuous Berth Allocation Problem and Quay Crane Scheduling with Non Crossing Constraint

Background/Objectives: This paper focused on integrated Continuous Berth Allocation Problem (BAPC) and Quay Crane Scheduling Problem (QCSP) by considering non-crossing constraint to make it more realistic. Methods/Statistical analysis: Genetic Algorithm (GA) is a metaheuristic method that has been used extensively in Berth Allocation Problem (BAP). Crossover and mutation are selected as operators in this paper. Findings: The integrated model is formulated as a Mix Integer Problem (MIP) with the objective to minimize the sum of the processing times. A vessel's processing time is measured between arrival and departure includingwaiting time to be berthed and servicing time.The new algorithm of GA arecompatible with the integrated model and useful for finding near-optimal solutions. Three phase new algorithms of GA are proposed and provide a wider search to the solution space. Application/Improvements: Three phase of GA is another significant and promising variant of genetic algorithms in BAPC and QCSP. The probabilities of crossover and mutation determine the degree of solution accuracy and the convergence speed that GA canobtain. By using fixed values of crossover and mutation, the algorithm utilize the population information in each generation and adaptively adjust the crossover and mutation. So, the population diversity and sustain the convergence capacity is maintained.