Linear Ranking Selection Research Articles

Congestion Aware MANET Routing Using Evolutionary Game Theory and Cross-Layer Design

A wireless network without infrastructure is called MANET. In MANET, a node can move in any direction, at any speed, and at any time. There is no separate router on this network. Instead, the nodes act as a router. To transfer data in the network in an efficient manner, the network should be congestion-free or less congested. If the network has congestion, it would affect the performance of the network. In the routing process, if a node is aware of the neighbour node’s congestion and selects a congestion-free path, it leads to better performance of the network. In this paper, we have designed two approaches for improving the performance of the network. One is the cross-layer protocol and the other is the evolutionary game theory approach to find the least congestion path. Cross-layer involves transport layer and MAC layer of MANET to avoid congestion. The Linear Rank Selection method is one of the evolutionary game theory approaches used to find the Least Congested Node. We have enhanced this strategy in the GPSR protocol. The proposed EVO GPSR protocol performs 4.65% better throughput, 0.54% less network delay, 2.10% less routing overhead, and 8.20% better packet delivery ratio compared with GPSR protocol. Based on simulation, the performance of the proposed protocol is better than the GPSR protocol.

Joint Estimation of Hydraulic and Biochemical Parameters for Reactive Transport Modelling with a Modified ILUES Algorithm

Multicomponent reactive transport modeling is a powerful tool for the comprehensive analysis of coupled hydraulic and biochemical processes. The performance of the simulation model depends on the accuracy of related model parameters whose values are usually difficult to determine from direct measurements. In this situation, estimates of these uncertain parameters can be obtained by solving inverse problems. In this study, an efficient data assimilation method, the iterative local updating ensemble smoother (ILUES), is employed for the joint estimation of hydraulic parameters, biochemical parameters and contaminant source characteristics in the sequential biodegradation process of tetrachloroethene (PCE). In the framework of the ILUES algorithm, parameter estimation is realized by updating local ensemble with the iterative ensemble smoother (IES). To better explore the parameter space, the original ILUES algorithm is modified by determining the local ensemble partly with a linear ranking selection scheme. Numerical case studies based on the sequential biodegradation of PCE are then used to evaluate the performance of the ILUES algorithm. The results show that the ILUES algorithm is able to achieve an accurate joint estimation of related model parameters in the reactive transport model.

Trade-off between exploration and exploitation with genetic algorithm using a novel selection operator

As an intelligent search optimization technique, genetic algorithm (GA) is an important approach for non-deterministic polynomial (NP-hard) and complex nature optimization problems. GA has some internal weakness such as premature convergence and low computation efficiency, etc. Improving the performance of GA is a vital topic for complex nature optimization problems. The selection operator is a crucial strategy in GA, because it has a vital role in exploring the new areas of the search space and converges the algorithm, as well. The fitness proportional selection scheme has essence exploitation and the linear rank selection is influenced by exploration. In this article, we proposed a new selection scheme which is the optimal combination of exploration and exploitation. This eliminates the fitness scaling issue and adjusts the selection pressure throughout the selection phase. The chi ^2 goodness-of-fit test is used to measure the average accuracy, i.e., mean difference between the actual and expected number of offspring. A comparison of the performance of the proposed scheme along with some conventional selection procedures was made using TSPLIB instances. The application of this new operator gives much more effective results regarding the average and standard deviation values. In addition, a two-tailed t test is established and its values showed the significantly improved performance by the proposed scheme. Thus, the new operator is suitable and comparable to established selection for the problems related to traveling salesman problem using GA.

Selective Pressure Strategy in differential evolution: Exploitation improvement in solving global optimization problems

The paper proposes a modification of Differential Evolution mutation strategies with the introduction of selective pressure, which is implemented by applying proportional, rank-based and tournament selection. Based on the new mutation strategies, a new algorithm called LSHADE-SP is proposed, which is a modification of the LSHADE algorithm, with various types of selective pressure implementation. The algorithm is tested against the Congress on Evolutionary Computation (CEC) 2017 competition on real-parameter optimization benchmark functions to demonstrate the advantage of using selective pressure. The comparison shows that applying linear rank, exponential rank and tournament selection deliver faster convergence, if a proper selective pressure is applied. The experiments were conducted for both classical mutation strategies, like rand/1 and best/1, and the best state-of-the art strategies, with various parameter adaptations. The results demonstrate that the algorithm with selective pressure is superior to the best state-of-the-art non-hybrid DE algorithms. The resulting algorithm, LSHADE-SP, obtained one of the best results among the algorithms that were winners of the CEC 2017 competition on real-parameter bound-constrained optimization.

Natural selection methods for Grey Wolf Optimizer

The selection process is the most attractive operator in the optimization algorithms. It normally mimics the natural selection of survival of the fittest principle. When the selection is too greedy, the selection pressure will be high and therefore the search becomes biased toward exploitation. In contrast, when the selection has a tendency to be random, the selection pressure will be low and thus the exploration is observed more. The selection process in Grey Wolf Optimizer (GWO) tends to be too greedy since the search is driven by the three best solutions. In this paper, different selection methods extracted from other evolutionary algorithms (EAs) are investigated for GWO. Along with the original selection method of GWO called greedy-based selection method, five others selection methods which are proportional, tournament, universal sampling, linear rank, and random selection methods are investigated. Accordingly, six versions of GWO are proposed which are Greedy-based GWO (GGWO), Proportional-based GWO (PGWO), Tournament-based GWO (TGWO), Universal sampling-based GWO (UGWO), Linear rank-based GWO (LGWO), Random-based GWO (RGWO). The six versions are evaluated using 23 test functions circulated in the literature with different characteristics and complexity. The sensitivity analysis of the control parameters of some new versions are discussed. Interestingly, TGWO achieved the best results for almost all benchmark functions. This proves that the selection methods have a high impact on the performance of GWO.

Estimation of distribution algorithm with path relinking for the blocking flow-shop scheduling problem

ABSTRACTThis article presents an effective estimation of distribution algorithm, named P-EDA, to solve the blocking flow-shop scheduling problem (BFSP) with the makespan criterion. In the P-EDA, a Nawaz–Enscore–Ham (NEH)-based heuristic and the random method are combined to generate the initial population. Based on several superior individuals provided by a modified linear rank selection, a probabilistic model is constructed to describe the probabilistic distribution of the promising solution space. The path relinking technique is incorporated into EDA to avoid blindness of the search and improve the convergence property. A modified referenced local search is designed to enhance the local exploitation. Moreover, a diversity-maintaining scheme is introduced into EDA to avoid deterioration of the population. Finally, the parameters of the proposed P-EDA are calibrated using a design of experiments approach. Simulation results and comparisons with some well-performing algorithms demonstrate the effectiveness of the P-EDA for solving BFSP.

Novel Genetic Algorithms for Loading Pattern Optimization Using State-of-the-Art Operators and a Simple Test Case

Novel genetic algorithms (GAs) are developed by using state-of-the-art selection and crossover operators, e.g., rank selection or tournament selection instead of the traditional roulette (fitness proportionate (FP)) selection operator and novel crossover and mutation operators by considering the chromosomes as permutations (which is a specific feature of the loading pattern (LP) problem). The algorithm is applied to a representative model of a modern pressurized water reactor (PWR) core and implemented using a single objective fitness function (FF), i.e., keff. The results obtained for some reference cases using this setup are excellent. They are obtained using a tournament selection operator with a linear ranking (LR) selection probability method and a new geometric crossover operator that allows for geometrical, rather than random, swaps of gene segments between the chromosomes and control over the sizes of the swapped segments. Finally, the effect of boundary conditions (BCs) on the symmetry of the obtained best solutions is studied and the validity of the “symmetric loading patterns” assumption is tested.

Performance Evaluation of WMN-GA System in Node Placement in WMNs for Different Distributions of Mesh Clients and Different Selection and Mutation Operators

Wireless Mesh Networks (WMNs) have become an important networking infrastructure due to their low cost for providing broadband connectivity. Issues for achieving the network connectivity and user coverage are related to the node placement problem. Several optimization problems are showing their usefulness to the efficient design of WMNs. These problems are related to optimizing network connectivity, user coverage and stability. In this paper, we formulate the optimization problems using a multi-objective optimization model. For the mesh router nodes placement, the bi-objective optimization problem is obtained consisting in the maximization of the size of the giant component in the mesh routers network (for measuring network connectivity) and that of user coverage. We evaluate the performance of WMN-GA system for node placement problem in WMNs. For evaluation, we consider Normal, Exponential and Weibull Distribution of mesh clients and different selection and mutation operators. The population size is considered 64 and the number of generation 200. The simulation results show that WMN-GA system performs better for Single Mutation, Linear Ranking selection and Normal distribution of mesh clients.

A Multi-Population Genetic Algorithm for Inducing Balanced Decision Trees on Telecommunications Churn Data

In this paper we present a new approach to predicting telecommunications churn. Churn prediction can be considered as a multi-objective optimization problem, where the accuracy of predicting both churning and staying consumers need to be optimized simultaneously. As the existing classification methods failed to produce balanced solutions, we developed a new multi-population genetic algorithm for the induction of decision trees. By introducing multiple populations, linear ranking selection and adequate fitness function we were able to avoid overly biased solutions. The evaluation results of our algorithm’s performance in comparison with the existing methods show that it was able to find highly accurate and balanced solutions. DOI: http://dx.doi.org/10.5755/j01.eee.19.6.4578

Analysis of takeover time and convergence rate for harmony search with novel selection methods

Recently, common selection schemes used in harmony search algorithm (HSA) are altered in memory consideration operation to imitate the natural selection principle of survival of the fittest. The selection schemes adopted include: random, proportional, tournament, and linear rank. In this paper, these selection schemes are analysed in order to evaluate their effect on the performance of HSA. The analysis considers takeover time and convergence rate to measure the effectiveness of each selection scheme. Furthermore, a scaled proportional selection scheme is proposed to replace the proportional selection scheme to overcome its shortcoming with negative fitness values. To study the effect of these different selection schemes we use eight global optimisation functions with different characteristics. An experimental evaluation show that linear rank selection provides the highest convergence speed and highest takeover time. On the other hand, scaled proportional selection provides the slowest convergence speed and slowest takeover time. This indicates the effect of the type of the selection method used in memory consideration in takeover time and convergence rate.

Multimodel Parameters Identification for Main Steam Temperature of Ultra-Supercritical Units Using an Improved Genetic Algorithm

AbstractA parameter identification method based on genetic algorithm (GA) is presented to solve the multimodel parameters identification problem for the main steam temperature of ultra-supercritical (USC) units. Linear ranking selection, nonuniform linear crossover, and Gaussian mutation are employed in the algorithm design to enhance the convergence speed and the accuracy of the identification. Besides, the uniform design method is executed to initialize the population and the sigmoid function with adaptation is applied to adjust the probabilities of crossover and mutation. Simulations carried out with the field operation data from Haimen USC units, including two processes—parameters identification and model verification. The simulation results show that the improved genetic algorithm performs well in global parameters searching and the proposed identification methodology offers good results for the multimodel parameters identification.

On the Impact of Mutation-Selection Balance on the Runtime of Evolutionary Algorithms

The interplay between mutation and selection plays a fundamental role in the behavior of evolutionary algorithms (EAs). However, this interplay is still not completely understood. This paper presents a rigorous runtime analysis of a non-elitist population-based EA that uses the linear ranking selection mechanism. The analysis focuses on how the balance between parameter η, controlling the selection pressure in linear ranking, and parameter χ controlling the bit-wise mutation rate, impacts the runtime of the algorithm. The results point out situations where a correct balance between selection pressure and mutation rate is essential for finding the optimal solution in polynomial time. In particular, it is shown that there exist fitness functions which can only be solved in polynomial time if the ratio between parameters η and χ is within a narrow critical interval, and where a small change in this ratio can increase the runtime exponentially. Furthermore, it is shown quantitatively how the appropriate parameter choice depends on the characteristics of the fitness function. In addition to the original results on the runtime of EAs, this paper also introduces a very useful analytical tool, i.e., multi-type branching processes, to the runtime analysis of non-elitist population-based EAs.

Applying a real‐coded multi‐population genetic algorithm to multi‐reservoir operation

AbstractThe primary objective of this study is to propose a real‐coded hypercubic distributed genetic algorithm (HDGA) for optimizing reservoir operation system. A conventional genetic algorithm (GA) is often trapped into local optimums during the optimization procedure. To prevent premature convergence and to obtain near‐global optimal solutions, the HDGA is designed to have various subpopulations that are processed using separate and parallel GAs. The hypercubic topology with a small diameter spreads good solutions rapidly throughout all of the subpopulations, and a migration mechanism, which exchanges chromosomes among the subpopulations, exchanges information during the joint optimization to maintain diversity and thus avoid a systematic premature convergence toward a single local optimum. Three genetic operators, i.e. linear ranking selection, blend‐α crossover and Gaussian mutation, are applied to search for the optimal reservoir releases. First, a benchmark problem, the four‐reservoir operation system, is considered to investigate the applicability and effectiveness of the proposed approach. The results show that the known global optimal solution can be effectively and stably achieved by the HDGA. The HDGA is then applied in the planning of a multi‐reservoir system in northern Taiwan, considering a water reservoir development scenario to the year 2021. The results searched by an HDGA minimize the water deficit of this reservoir system and provide much better performance than the conventional GA in terms of obtaining lower values of the objective function and avoiding local optimal solutions. Copyright © 2006 John Wiley & Sons, Ltd.

Calculating the expected loss of diversity of selection schemes.

This paper concerns a measure of selective pressure, called "loss of diversity," that denotes the proportion of unselected individuals during the selection phase. We probabilistically calculate the expected value and variance of loss of diversity in tournament selection, truncation selection, linear ranking selection, and exponential ranking selection. From numerical results, we observe that in tournament selection, many more individuals are expected to be lost than with Blickle and Thiele's static estimate. We also observe that tournament and exponential ranking schemes potentially bring about nearly equivalent selection behaviors but have different types of control parameters.

Analysis of Selection Algorithms: A Markov Chain Approach

A Markov chain framework is developed for analyzing a wide variety of selection techniques used in genetic algorithms (GAs) and evolution strategies (ESs). Specifically, we consider linear ranking selection, probabilistic binary tournament selection, deterministic s-ary (s = 3,4, …) tournament selection, fitness-proportionate selection, selection in Whitley's GENITOR, selection in (μ, λ)-ES, selection in (μ + λ)-ES, (μ, λ)-linear ranking selection in GAs, (μ + λ)-linear ranking selection in GAs, and selection in Eshelman's CHC algorithm. The analysis enables us to compare and contrast the various selection algorithms with respect to several performance measures based on the probability of takeover. Our analysis is exact—we do not make any assumptions or approximations. Finite population sizes are considered. Our approach is perfectly general, and following the methods of this paper, it is possible to analyze any selection strategy in evolutionary algorithms.

Genetic Algorithms, Selection Schemes, and the Varying Effects of Noise

This paper analyzes the effect of noise on different selection mechanisms for genetic algorithms (GAs). Models for several selection schemes are developed that successfully predict the convergence characteristics of GAs within noisy environments. The selection schemes modeled in this paper include proportionate selection, tournament selection, (μ, λ) selection, and linear ranking selection. An allele-wise model for convergence in the presence of noise is developed for the OneMax domain, and then extended to more complex domains where the building blocks are uniformly scaled. These models are shown to accurately predict the convergence rate of GAs for a wide range of noise levels.