Basic Evolutionary Algorithm Research Articles

A Comparative Study of Optimal PV Allocation in a Distribution Network Using Evolutionary Algorithms

The growing distributed energy resource (DER) penetration into distribution networks, such as through residential and commercial photovoltaics (PV), has emerged through a transition from passive to active networks, which takes the complexity of planning and operations to the next level. Optimal PV allocation (sizing and location) is challenging because it involves mixed-integer non-linear programming with three-phase non-linear unbalanced power flow equations. Meta-heuristic algorithms have proven their effectiveness in many complex engineering problems. Thus, in this study, we propose to achieve optimal PV allocation by using several basic evolutionary algorithms (EAs), particle swarm optimization (PSO), artificial bee colony (ABC), differential evolution (DE), and their variants, all of which are applied for a study of their performance levels. Two modified unbalanced IEEE test feeders (13 and 37 bus) are developed to evaluate these performance levels, with two objectives: one is to maximize PV penetration, and the other is to minimize the voltage deviation from 1.0 p.u. To handle the computational burden of the sequential power flow and unbalanced network, we adopt an efficient iterative load flow algorithm instead of the commonly used and yet highly simplified forward–backward sweep method. A comparative study of these basic EAs shows their general success in finding a near-optimal solution, except in the case of the DE, which is known for solving continuous optimization problems efficiently. From experiments run 30 times, it is observed that PSO-related algorithms are more efficient and robust in the maximum PV penetration case, while ABC-related algorithms are more efficient and robust in the minimum voltage deviation case.

Technical note: Graph-theory-based heuristics to aid in the implementation of optimized drinking water network sectorization

Abstract. Drinking water distribution networks form an essential part of modern-day critical infrastructure. Sectorizing a network into district metered areas is a key technique for pressure management and water loss reduction. Sectorizing an existing network from scratch is, however, an exceedingly complex design task that designs in a well-studied general mathematical problem. Numerical optimization techniques such as evolutionary algorithms can be used to search for near-optimal solutions to such problems, but doing so within a reasonable time frame remains an ongoing challenge. In this work, we introduce two heuristic tricks that use information of the network structure and information of the operational requirements of the drinking water distribution network to modify the basic evolutionary algorithm used to solve the general problem. These techniques not only reduce the time required to find good solutions but also ensure that these solutions better match the requirements of drinking water practice. Both techniques were demonstrated by applying them in the sectorization of the actual distribution network of a large city.

A multi-staged niched evolutionary approach for allocating parallel tasks with joint optimization of performance, energy, and temperature

This paper presents a multi-stage multi-objective evolutionary approach (MS-MOEA) for allocating parallel computations on multi-core processors by joint optimizing performance (P), energy (E), and temperature (T). Evolutionary techniques have been shown to be effective for solving optimization problems, including our own previous work on solving the PET-optimized scheduling (PETOS) problem. There have long been a great many debates and rivalries between various evolutionary approaches, such as the SPEA or NSGA, with regard to their relative matters. The novelty of the proposed MS-MOEA approach is its amalgamation of the basic evolutionary algorithms that are already shown to be highly effective, thereby creating a niche of these techniques. The niche takes advantages of the strengths of each baseline technique for achieving additional enhancement in the precision of the optimization. We propose six multi-stage hybrids, each designed with either niched fitness assignment strategy, or combining populations from multiple MOEAs, or incorporating the problem knowledge into the conventional technique. The experimental results measure the quality of resulting Pareto fronts and demonstrate that the proposed MS-MOEAs yield better optimization for the PETOS problem in achieving three-objective in parallel task-to-core mapping.

Identification of NARMAX Hammerstein models with performance assessment using brain storm optimization algorithm

SummaryIn this paper, brain storm optimization (BSO)‐based efficient identification approach has been applied to different types of stable and practically useful Nonlinear Auto Regressive Moving Average with exogenous noise (NARMAX) Hammerstein models with various performance criteria‐based assessments. Different performance measures of the estimation process like accuracy, precision and consistency have been established to ensure the general applicability and practical usefulness of the proposed approach. The accuracy and the precision of the parameter estimation are established with the corresponding bias and variance information, while the consistency has been justified with the help of hypothesis test results. BSO‐based optimum values of the output mean square errors and the parameters and their corresponding convergences ensure the stability and robustness of the proposed identification scheme. The comparative studies of the performance of the BSO algorithm with the other basic evolutionary algorithms have been reported with optimum values of the mean square errors, estimated values of the parameters, corresponding computational times and hypothesis test outcomes. Copyright © 2016 John Wiley & Sons, Ltd.

Towards Fast Niching Evolutionary Algorithms: A Locality Sensitive Hashing-Based Approach

Niching techniques have recently been incorporated into evolutionary algorithms (EAs) for multisolution optimization in multimodal landscape. However, existing niching techniques inevitably increase the time complexity of basic EAs due to the computation of the distance matrix of individuals. In this paper, we propose a fast niching technique. The technique avoids pairwise distance calculations by introducing the locality sensitive hashing, an efficient algorithm for approximately retrieving nearest neighbors. Individuals are projected to a number of buckets by hash functions. The similar individuals possess a higher probability of being hashed into the same bucket than the dissimilar ones. Then, interactions between individuals are limited to the candidates that fall in the same bucket to achieve local evolution. It is proved that the complexity of the proposed fast niching is linear to the population size. In addition, this mechanism induces stable niching behavior and it inherently keeps a balance between the exploration and exploitation of multiple optima. The theoretical analysis conducted in this paper suggests that the proposed technique is able to provide bounds for the exploration and exploitation probabilities. Experimental results show that the fast niching versions of the multimodal algorithms can exhibit similar or even better performance than their original ones. More importantly, the execution time of the algorithms is significantly reduced.

Multi-objectivization, fitness landscape transformation and search performance: A case of study on the hp model for protein structure prediction

Abstract Multi-objectivization represents a current and promising research direction which has led to the development of more competitive search mechanisms. This concept involves the restatement of a single-objective problem in an alternative multi-objective form, which can facilitate the process of finding a solution to the original problem. Recently, this transformation was applied with success to the HP model, a simplified yet challenging representation of the protein structure prediction problem. The use of alternative multi-objective formulations, based on the decomposition of the original objective function of the problem, has significantly increased the performance of search algorithms. The present study goes further on this topic. With the primary aim of understanding and quantifying the potential effects of multi-objectivization, a detailed analysis is first conducted to evaluate the extent to which this problem transformation impacts on an important characteristic of the fitness landscape, neutrality. To the authors’ knowledge, the effects of multi-objectivization have not been previously investigated by explicitly sampling and evaluating the neutrality of the fitness landscape. Although focused on the HP model, most of the findings of such an analysis can be extrapolated to other problem domains, contributing thus to the general understanding of multi-objectivization. Finally, this study presents a comparative analysis where the advantages of multi-objectivization are evaluated in terms of the performance of a basic evolutionary algorithm. Both the two- and three-dimensional variants of the HP model (based on the square and cubic lattices, respectively) are considered.

Research on Chaos Theory Improved Evolutionary Algorithm

Because the basic evolutionary algorithm convergence speed is slow, prone to stagnation, the algorithm running time is too long, so according to chaos theory about the relationship between evolution and chaos, this paper design an improved evolutionary algorithm with chaotic mutation operator, the optimization of the contraction policy can improve the global search ability of effective, significantly improved the performance of the proposed algorithm.

Two-Level Production Plan Decomposition Based on a Hybrid MOEA for Mineral Processing

This paper addresses the integrated multiobjective production planning and decomposing (P&D) problem for mineral processing. A novel multiobjective 0-1 mixed integer nonlinear programming model is presented for the simultaneous P&D problem (O-model). In order to reduce the computational cost for solving O-model, a rolling horizon-based two-level decomposition approach is proposed to separate O-model into an upper level model (H-model) and a lower level model (L-model). An interactive partition (IP) and multiobjective gradient (MO-G)-based hybrid evolutionary multiobjective (EMO) algorithm named as IG-NSGA-II/IG-SPEA2, which takes the popular NSGA-II/SPEA2 as the basic EA, is proposed to solve both H-model and L-model, where an IP technique is designed to generate the efficient feasible combinational nodes, an ideal solution technique is provided for fathoming the infeasible nodes, an improved multiobjective gradient-based operator is developed to accelerate the evolution process in each selected node, and a cut with all continuous variables is constructed to exclude the previous feasible combination if it is not desired for the decision makers (DMs). The experimental results demonstrate that the presented two-level decomposition strategy can effectively integrate both levels. Moreover, the proposed hybrid method can effectively reduce the combinatorial space so as to concentrate the computing resource on the subspace of most interest, and can generate better feasible solutions than the pure EA in the full-space under computation time limits.

Revisiting the Restricted Growth Function Genetic Algorithm for Grouping Problems

An overview of the restricted growth function genetic algorithm is given. Empirically we show that the algorithm exhibits poor performance and is consistently outperformed on a range of problems by two very basic evolutionary algorithms with blind operators.

Alopex-based evolutionary algorithm and its application to reaction kinetic parameter estimation

In this paper, a novel Alopex-based evolutionary algorithm (AEA) is proposed, whose distinguished features are stochastic selection and self-adaptive evolutionary computation. The AEA not only inherits the primary characteristics of basic evolutionary algorithms (EAs), but also possesses the merits of gradient methods and simulated annealing algorithm. It can efficiently maintain the population diversity and improve the capabilities of escaping from local optima. The numerical simulation results of 22 benchmark functions demonstrate that the performance of the proposed AEA is superior to that of the basic EAs. Finally, the new algorithm is applied to estimate the kinetic parameters of 2-chlorophenol oxidation of supercritical water. The promising results illustrate the efficiency of the proposed method and show that it could be used as a reliable tool for engineering applications.

Comparative Analysis of Evolutionary, Local Search, and Hybrid Approaches to O/D Traffic Estimation

This paper compares the performance of various advanced evolutionary algorithm (EA) techniques to solve the problem of dynamic origin/destination (O/D) estimation. The potential of EA in the dynamic O/D estimation problem lies in their powerful global search and optimization capabilities. This EA-based demand estimation framework is implemented into a model that we call dynamic O/D estimator (DynODE). DynODE is integrated with an existing dynamic traffic assignment (DTA) platform (i.e., Dynasmart-P). The EA-based methods in this research are further augmented with EA parallelization as well as hybridization schemes to further improve the quality and efficiency of the solution. In this paper, we evaluate the performance of the basic EA model, the hybrid EA model, the parallel EA (PEA) model, as well as the parallel hybrid EA model. Additionally, the performance of a local-search method is examined and compared with the EA-based approaches. The comparison is performed on a medium size network. The results from the study show that the PEA model outperforms the other algorithms in terms of speed as well as solution quality. However, all hybrid and PEA runs result in savings in computation resources as well as enhancement in the quality of solution as compared to the basic EA model. Still, the basic EA model outperforms the local-search model.

Fast Multi-objective Rescheduling of Workflows to Constrained Resources Using Heuristics and Memetic Evolution

Scheduling of jobs organized in workflows to a computational grid is a permanent process due to the dynamic nature of the grid and the frequent arrival of new jobs. Thus, a permanent rescheduling of already planned and new jobs must be performed. This paper will continue and extend previous work, which focused on the tuning of our G lobal O ptimising R esource B roker and A llocator GORBA in a static planning environment. A formal definition of the scheduling problem and a classification will be given. New heuristics for rescheduling based on the old plan will be introduced and it will be investigated how they contribute to the overall planning process. As an extension to the work published in Conf. Proc. PPAM 2009, LNCS 6067 or 6068 (to be published in July, 2010) a simple local search is added to the basic Evolutionary Algorithm (EA) of GORBA and it is examined, whether and how the resulting Memetic Algorithm improves the results within the limited time frame of three minutes available for planning. Furthermore, the maximal possible load, which can be handled within the given planning time, will be examined for a grid of growing size of up to 7000 grid jobs and 700 resources.

Algorithmic analysis of a basic evolutionary algorithm for continuous optimization

In practical optimization, applying evolutionary algorithms has nearly become a matter of course. Their theoretical analysis, however, is far behind practice. So far, theorems on the runtime are limited to discrete search spaces; results for continuous search spaces are limited to convergence theory or even rely on validation by experiments, which is unsatisfactory from a theoretical point of view. The simplest, or most basic, evolutionary algorithms use a population consisting of only one individual and use random mutations as the only search operator. Here the so-called (1+1) evolution strategy for minimization in R n is investigated when it uses isotropically distributed mutation vectors. In particular, so-called Gaussian mutations are analyzed when the so-called 1/5-rule is used for their adaptation. Obviously, a reasonable analysis must respect the function to be minimized, and furthermore, the runtime must be measured with respect to the approximation error. A first algorithmic analysis of how the runtime depends on n , the dimension of the search space, is presented. This analysis covers all unimodal functions that are monotone with respect to the distance from the optimum. It turns out that, in the scenario considered, Gaussian mutations in combination with the 1/5-rule indeed ensure asymptotically optimal runtime; namely, Θ ( n ) steps/function evaluations are necessary and sufficient to halve the approximation error.

Application des algorithmes évolutionnaires pour l'estimation du mouvement à l'aide des champs de Markov

This paper presents a new optical flow estimation method using Markov Random Fields (MRFs) modelling. In the MRF framework, optical flow estimation leads to minimizing an energy function. To this end, we propose an Evolutionary Algorithm (EA) based on a Divide-and-Conquer strategy which adequately exploits the Markovian property. The image is split into small parts on which EAs are performed separately and independently. The elementary results obtained from all image parts are then combined into a final global solution. Experimental results using synthetic and real-world image sequences demonstrate the effectiveness of the method. Compared with traditional ICM and simulated annealing, results are improved. Moreover, computation cost is significantly reduced in comparison with a basic EA.

Structural optimization using evolutionary algorithms

The objective of this paper is to investigate the efficiency of various evolutionary algorithms (EA), such as genetic algorithms and evolution strategies, when applied to large-scale structural sizing optimization problems. Both type of algorithms imitate biological evolution in nature and combine the concept of artificial survival of the fittest with evolutionary operators to form a robust search mechanism. In this paper modified versions of the basic EA are implemented to improve the performance of the optimization procedure. The modified versions of both genetic algorithms and evolution strategies combined with a mathematical programming method to form hybrid methodologies are also tested and compared and proved particularly promising. The numerical tests presented demonstrate the computational advantages of the discussed methods, which become more pronounced in large-scale optimization problems.

Large scale structural optimization: Computational methods and optimization algorithms

The objective of this paper is to investigate the efficiency of various optimization methods based on mathematical programming and evolutionary algorithms for solving structural optimization problems under static and seismic loading conditions. Particular emphasis is given on modified versions of the basic evolutionary algorithms aiming at improving the performance of the optimization procedure. Modified versions of both genetic algorithms and evolution strategies combined with mathematical programming methods to form hybrid methodologies are also tested and compared and proved particularly promising. Furthermore, the structural analysis phase is replaced by a neural network prediction for the computation of the necessary data required by the evolutionary algorithms. Advanced domain decomposition techniques particularly tailored for parallel solution of large-scale sensitivity analysis problems are also implemented. The efficiency of a rigorous approach for treating seismic loading is investigated and compared with a simplified dynamic analysis adopted by seismic codes in the framework of finding the optimum design of structures with minimum weight. In this context a number of accelerograms are produced from the elastic design response spectrum of the region. These accelerograms constitute the multiple loading conditions under which the structures are optimally designed. The numerical tests presented demonstrate the computational advantages of the discussed methods, which become more pronounced in large-scale optimization problems.