Paired-bacteria optimiser - A simple and fast algorithm

Most of Evolutionary Algorithms (EAs) do not fully explore the potential of searching ability and are time consuming. This paper presents a fast bacteria-inspired optimisation algorithm: Paired-Bacteria Optimiser (PBO), which incorporates the underlying mechanisms of bacterial chemotaxis and quorum sensing, and has only two bacteria in a population. The experimental results show that PBO has not only a promising performance of searching for accurate solutions, but also a fast convergence rate, which are evaluated using benchmark functions.     

Improved differential evolution approach based on cultural algorithm and diversity measure applied to solve economic load dispatch problems

Evolutionary algorithms (EAs) are general-purpose stochastic search methods that use the metaphor of evolution as the key element in the design and implementation of computer-based problems solving systems. During the past two decades, EAs have attracted much attention and wide applications in a variety of fields, especially for optimization and design. EAs offer a number of advantages: robust and reliable performance, global search capability, little or no information requirement, and others. Among various EAs, differential evolution (DE), which characterized by the different mutation operator and competition strategy from the other EAs, has shown great promise in many numerical benchmark problems and real-world optimization applications. The potentialities of DE are its simple structure, easy use, convergence speed and robustness. To improve the global optimization property of DE, in this paper, a DE approach based on measure of population's diversity and cultural algorithm technique using normative and situational knowledge sources is proposed as alternative method to solving the economic load dispatch problems of thermal generators. The traditional and cultural DE approaches are validated for two test systems consisting of 13 and 40 thermal generators whose nonsmooth fuel cost function takes into account the valve-point loading effects. Simulation results indicate that performance of the cultural DE present best results when compared with previous optimization approaches in solving economic load dispatch problems.     

A nature-inspired multi-objective optimisation strategy based on a new reduced space searching algorithm for the design of alloy steels

In this paper, a salient search and optimisation algorithm based on a new reduced space searching strategy, is presented. This algorithm originates from an idea which relates to a simple experience when humans search for an optimal solution to a ‘real-life’ problem, i.e. when humans search for a candidate solution given a certain objective, a large area tends to be scanned first; should one succeed in finding clues in relation to the predefined objective, then the search space is greatly reduced for a more detailed search. Furthermore, this new algorithm is extended to the multi-objective optimisation case. Simulation results of optimising some challenging benchmark problems suggest that both the proposed single-objective and multi-objective optimisation algorithms outperform some of the other well-known Evolutionary Algorithms (EAs). The proposed algorithms are further applied successfully to the optimal design problem of alloy steels, which aims at determining the optimal heat treatment regime and the required weight percentages for chemical composites to obtain the desired mechanical properties of steel hence minimising production costs and achieving the overarching aim of ‘right-first-time production’ of metals.     

Evolutionary design of Evolutionary Algorithms

Manual design of Evolutionary Algorithms (EAs) capable of performing very well on a wide range of problems is a difficult task. This is why we have to find other manners to construct algorithms that perform very well on some problems. One possibility (which is explored in this paper) is to let the evolution discover the optimal structure and parameters of the EA used for solving a specific problem. To this end a new model for automatic generation of EAs by evolutionary means is proposed here. The model is based on a simple Genetic Algorithm (GA). Every GA chromosome encodes an EA, which is used for solving a particular problem. Several Evolutionary Algorithms for function optimization are generated by using the considered model. Numerical experiments show that the EAs perform similarly and sometimes even better than standard approaches for several well-known benchmarking problems.     

进化神经网络研究综述

进化算法(EAs)与神经网络(NN)的结合已形成了一个新的领域一进化神经网络,在神经网络的研究中举足轻重。本文通过讨论和总结进化神经网络中的关键技术和现状，概述了其设计与构造的趋势。所讨论的是：(1)进化神经网络的研究方法；(2)进化模型；(3)应用实例及关键技术；(4)研究方向。     

Evolution of mathematical models of chaotic systems based on multiobjective genetic programming

This work is concerned with the identification of models for nonlinear dynamical systems using multiobjective evolutionary algorithms. Systems modelling involves the processes of structure selection, parameter estimation, model performance and model validation and involves a complex solution space. Evolutionary Algorithms (EAs) are search and optimisation tools founded on the principles of natural evolution and genetics, which are suitable for a wide range of application areas. Due to the versatility of these tools and motivated by the versatility of genetic programming (GP), this evolutionary paradigm is proposed for this modelling problem. GP is then combined with a multiobjective function definition scheme. Multiobjective genetic programming (MOGP) is applied to multiple, conflicting objectives and yields a set of candidate parsimonious and valid models, which reproduce the original system behaviour. The MOGP approach is then demonstrated as being applicable for system modelling with chaotic dynamics. The circuit introduced by Chua, being one of the most popular benchmarks for studying nonlinear oscillations, and the Duffing–Holmes oscillator are the systems to test the evolutionary-based modelling approach introduced in this paper.     

Integrating Variable Reduction Strategy With Evolutionary Algorithms for Solving Nonlinear Equations Systems

Nonlinear equations systems (NESs) are widely used in real-world problems and they are difficult to solve due to their nonlinearity and multiple roots. Evolutionary algorithms (EAs) are one of the methods for solving NESs, given their global search capabilities and ability to locate multiple roots of a NES simultaneously within one run. Currently, the majority of research on using EAs to solve NESs focuses on transformation techniques and improving the performance of the used EAs. By contrast, problem domain knowledge of NESs is investigated in this study, where we propose the incorporation of a variable reduction strategy (VRS) into EAs to solve NESs. The VRS makes full use of the systems of expressing a NES and uses some variables (i.e., core variable) to represent other variables (i.e., reduced variables) through variable relationships that exist in the equation systems. It enables the reduction of partial variables and equations and shrinks the decision space, thereby reducing the complexity of the problem and improving the search efficiency of the EAs. To test the effectiveness of VRS in dealing with NESs, this paper mainly integrates the VRS into two existing state-of-the-art EA methods (i.e., MONES and DR-JADE) according to the integration framework of the VRS and EA, respectively. Experimental results show that, with the assistance of the VRS, the EA methods can produce better results than the original methods and other compared methods. Furthermore, extensive experiments regarding the influence of different reduction schemes and EAs substantiate that a better EA for solving a NES with more reduced variables tends to provide better performance.      

Constrained,mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation

Reducing building energy demand is a crucial part of the global response to climate change, and evolutionary algorithms (EAs) coupled to building performance simulation (BPS) are an increasingly popular tool for this task. Further uptake of EAs in this industry is hindered by BPS being computationally intensive: optimisation runs taking days or longer are impractical in a time-competitive environment. Surrogate fitness models are a possible solution to this problem, but few approaches have been demonstrated for multi-objective, constrained or discrete problems, typical of the optimisation problems in building design. This paper presents a modified version of a surrogate based on radial basis function networks, combined with a deterministic scheme to deal with approximation error in the constraints by allowing some infeasible solutions in the population. Different combinations of these are integrated with Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and applied to three instances of a typical building optimisation problem. The comparisons show that the surrogate and constraint handling combined offer improved run-time and final solution quality. The paper concludes with detailed investigations of the constraint handling and fitness landscape to explain differences in performance.     

进化算法的计算结果呈现方式

进化算法（Evolutionary Algorithms,EAs）作为求解非线性规划问题的有效求解工具已经越来越受到工程和优化领域的国内外专家和学者的重视,进化算法类的文章在世界上各种期刊中占据了大量比例。目前仍有很多刚刚从事进化算法理论与实践方面研究的国内学者对如何表现进化算法的计算结果比较迷茫。为此对于算法的计算结果展现方面进行了阐述。     

通用进化算法的收敛性分析

传统进化算法的收敛性专注于具体算法,对应的研究成果也仅仅适用于具体算法。为了研究所有进化算法的收敛性问题,提出了一种包含所有操作类型算子的通用进化算法,建立了一套概率空间用于研究算法的收敛性,所有有关算法的术语都用严格的数学语言加以定义。在概率空间中,有七个算法收敛性定理被完整地证明,其中之一找到了算法依概率收敛的充分必要条件。更为重要的是,这些定理适用所有进化算法。它建立了一个体系,用来指导进化算法的设计,从理论上判断进化算法的收敛性。     

进化算法研究进展

进化算法是一类借鉴生物界自然选择和自然遗传机制的随机搜索算法，主要包括遗传算法，（ｇｅｎｅｒｉｃａｌｇｏｒｉｔｈｍｓ，简记为ＧＡｓ）、进化规划（ｅｖｏｌｕｔｉｏｎａｒｙｐｒｏｇｒａｍｍｉｎｇ，简记为ＥＰ）和进化策略（ｅｖｏｌｕｔｉｏｎａｒｙｓｔｒａｔｅｇｉｅｓ，简记为ＥＳｓ），它们可以用解决优化和机器学习等问题，进化算法的两个主要特点中群体搜索策略及群体中个体之间的信息交换，进化算法不依赖于梯度信     

Evolutionary algorithms for the design of grid-connected PV-systems

The sale of electric energy generated by photovoltaic (PV) plants has attracted much attention in recent years. The installation of PV plants aims to obtain the maximum benefit of captured solar energy. The current methodologies for planning the design of the different components of a PV plant are not completely efficient. This paper addresses the optimization of the design of PV plants with solar tracking, which consists of the optimization of the variables that make up the PV plant to obtain the minimum electric (Joule) losses possible. These variables are the size and distribution of solar modules in the solar tracker, the distribution of the solar trackers in the field and the choice of inverter. Evolutionary algorithms (EAs) are adaptive methods based on natural evolution that may be used for searching and optimization. Four different EAs have been used for optimizing the design of PV plants: steady-state genetic algorithm, generational genetic algorithm, CHC algorithm and DE algorithm. In order to test the performance of these algorithms we have used different proposed fields to mount PV plants. The results obtained show that EAs, and specifically DE with rand mutation schemes, are promising techniques to optimize design of PV plants. Furthermore, the results are contrasted with nonparametric statistical tests to support our conclusions.     

A bi-population based scheme for an explicit exploration/exploitation trade-off in dynamic environments

Optimisation in changing environments is a challenging research topic since many real-world problems are inherently dynamic. Inspired by the natural evolution process, evolutionary algorithms (EAs) are among the most successful and promising approaches that have addressed dynamic optimisation problems. However, managing the exploration/exploitation trade-off in EAs is still a prevalent issue, and this is due to the difficulties associated with the control and measurement of such a behaviour. The proposal of this paper is to achieve a balance between exploration and exploitation in an explicit manner. The idea is to use two equally sized populations: the first one performs exploration while the second one is responsible for exploitation. These tasks are alternated from one generation to the next one in a regular pattern, so as to obtain a balanced search engine. Besides, we reinforce the ability of our algorithm to quickly adapt after cnhanges by means of a memory of past solutions. Such a combination aims to restrain the premature convergence, to broaden the search area, and to speed up the optimisation. We show through computational experiments, and based on a series of dynamic problems and many performance measures, that our approach improves the performance of EAs and outperforms competing algorithms.     

Finding Mount Everest and handling voids

Evolutionary algorithms (EAs) are randomized search heuristics that solve problems successfully in many cases. Their behavior is often described in terms of strategies to find a high location on Earth's surface. Unfortunately, many digital elevation models describing it contain void elements. These are elements not assigned an elevation. Therefore, we design and analyze simple EAs with different strategies to handle such partially defined functions. They are experimentally investigated on a dataset describing the elevation of Earth's surface. The largest value found by an EA within a certain runtime is measured, and the median over a few runs is computed and compared for the different EAs. For the dataset, the distribution of void elements seems to be neither random nor adversarial. They are so-called semirandomly distributed. To deepen our understanding of the behavior of the different EAs, they are theoretically considered on well-known pseudo-Boolean functions transferred to partially defined ones. These modifications are also performed in a semirandom way. The typical runtime until an optimum is found by an EA is analyzed, namely bounded from above and below, and compared for the different EAs. We figure out that for the random model it is a good strategy to assume that a void element has a worse function value than all previous elements. Whereas for the adversary model it is a good strategy to assume that a void element has the best function value of all previous elements.     

Population Markov Chain Monte Carlo

Stochastic search algorithms inspired by physical and biological systems are applied to the problem of learning directed graphical probability models in the presence of missing observations and hidden variables. For this class of problems, deterministic search algorithms tend to halt at local optima, requiring random restarts to obtain solutions of acceptable quality. We compare three stochastic search algorithms: a Metropolis-Hastings Sampler (MHS), an Evolutionary Algorithm (EA), and a new hybrid algorithm called Population Markov Chain Monte Carlo, or popMCMC. PopMCMC uses statistical information from a population of MHSs to inform the proposal distributions for individual samplers in the population. Experimental results show that popMCMC and EAs learn more efficiently than the MHS with no information exchange. Populations of MCMC samplers exhibit more diversity than populations evolving according to EAs not satisfying physics-inspired local reversibility conditions.     

基于演化算法的客户关系管理系统研究

演化算法是一种模拟生物进化过程的自适应算法.客户关系管理系统是顺应企业改革发展需要的产物,在企业与客户之间建立起友好的桥梁,增进相互的了解和合作.本文通过对演化算法基本概念的描述,初步阐述了如何将其用于客户关系管理系统中,使系统更具有智能性、自适应性和可扩展性.     

A comparative study of many-objective evolutionary algorithms for the discovery of software architectures

During the design of complex systems, software architects have to deal with a tangle of abstract artefacts, measures and ideas to discover the most fitting underlying architecture. A common way to structure such complex systems is in terms of their interacting software components, whose composition and connections need to be properly adjusted. Along with the expected functionality, non-functional requirements are key at this stage to guide the many design alternatives to be evaluated by software architects. The appearance of Search Based Software Engineering (SBSE) brings an approach that supports the software engineer along the design process. Evolutionary algorithms can be applied to deal with the abstract and highly combinatorial optimisation problem of architecture discovery from a multiple objective perspective. The definition and resolution of many-objective optimisation problems is currently becoming an emerging challenge in SBSE, where the application of sophisticated techniques within the evolutionary computation field needs to be considered. In this paper, diverse non-functional requirements are selected to guide the evolutionary search, leading to the definition of several optimisation problems with up to 9 metrics concerning the architectural maintainability. An empirical study of the behaviour of 8 multi- and many-objective evolutionary algorithms is presented, where the quality and type of the returned solutions are analysed and discussed from the perspective of both the evolutionary performance and those aspects of interest to the expert. Results show how some many-objective evolutionary algorithms provide useful mechanisms to effectively explore design alternatives on highly dimensional objective spaces.     

Improving the efficiency of multi-objective evolutionary algorithms through decomposition: An application to water distribution network design

Evolutionary algorithms (EAs) have been widely used in handling various water resource optimization problems in recent years. However, it is still challenging for EAs to identify near-optimal solutions for realistic problems within the available computational budgets. This paper introduces a novel multi-objective optimization method to improve the efficiency of a typically difficult water resource problem: water distribution network (WDN) design. In the proposed approach, a WDN is decomposed into different sub-networks using decomposition techniques. EAs optimize these sub-networks individually, generating Pareto fronts for each sub-network with great efficiency. A propagation method is proposed to evolve Pareto fronts of the sub-networks towards the Pareto front for the full network while eliminating the need to hydraulically simulate the intact network itself. Results from two complex realistic WDNs show that the proposed approach is able to find better fronts than conventional full-search algorithms (optimize the entire network without decomposition) with dramatically improved efficiency.     

Quality of Service constrained routing optimization using Evolutionary Computation

In this work, a novel optimization framework is proposed that allows the improvement of Quality of Service levels in TCP/IP based networks, by configuring the routing weights of link-state protocols such as OSPF. Since this is a NP-hard problem, some algorithms from Evolutionary Computation were considered, working over a mathematical model that allows the definition of flexible cost functions that can take into account several measures of the network behaviour, such as network congestion and end-to-end delays. A number of experiments were performed, over a large set of network topologies, where Evolutionary Algorithms (EAs), Differential Evolution, local search methods and common heuristics were compared. EAs make the most promising alternative leading to solutions with an effective network performance, even under unfavourable scenarios. A number of state of the art multi-objective optimization algorithms were also tested, but the proposed EAs still hold as the most consistent method for network optimization.