Evolutionary Simulated Annealing Research Articles

Performance Analyses of Nature-inspired Algorithms on the Traveling Salesman’s Problems for Strategic Management

This paper carries out a performance analysis of major Nature-inspired Algorithms in solving the benchmark symmetric and asymmetric Traveling Salesman’s Problems (TSP). Knowledge of the workings of the TSP is very useful in strategic management as it provides useful guidance to planners. After critical assessments of the performances of eleven algorithms consisting of two heuristics (Randomized Insertion Algorithm and the Honey Bee Mating Optimization for the Travelling Salesman’s Problem), two trajectory algorithms (Simulated Annealing and Evolutionary Simulated Annealing) and seven population-based optimization algorithms (Genetic Algorithm, Artificial Bee Colony, African Buffalo Optimization, Bat Algorithm, Particle Swarm Optimization, Ant Colony Optimization and Firefly Algorithm) in solving the 60 popular and complex benchmark symmetric Travelling Salesman’s optimization problems out of the total 118 as well as all the 18 asymmetric Travelling Salesman’s Problems test cases available in TSPLIB91. The study reveals that the African Buffalo Optimization and the Ant Colony Optimization are the best in solving the symmetric TSP, which is similar to intelligence gathering channel in the strategic management of big organizations, while the Randomized Insertion Algorithm holds the best promise in asymmetric TSP instances akin to strategic information exchange channels in strategic management.

Cooperative evolutionary heterogeneous simulated annealing algorithm for google machine reassignment problem

This paper investigates the Google machine reassignment problem (GMRP). GMRP is a real world optimisation problem which is to maximise the usage of cloud machines. Since GMRP is computationally challenging problem and exact methods are only advisable for small instances, meta-heuristic algorithms have been used to address medium and large instances. This paper proposes a cooperative evolutionary heterogeneous simulated annealing (CHSA) algorithm for GMRP. The proposed algorithm consists of several components devised to generate high quality solutions. Firstly, a population of solutions is used to effectively explore the solution space. Secondly, CHSA uses a pool of heterogeneous simulated annealing algorithms in which each one starts from a different initial solution and has its own configuration. Thirdly, a cooperative mechanism is designed to allow parallel searches to share their best solutions. Finally, a restart strategy based on mutation operators is proposed to improve the search performance and diversification. The evaluation on 30 diverse real-world instances shows that the proposed CHSA performs better compared to cooperative homogeneous SA and heterogeneous SA with no cooperation. In addition, CHSA outperformed the current state-of-the-art algorithms, providing new best solutions for eleven instances. The analysis on algorithm behaviour clearly shows the benefits of the cooperative heterogeneous approach on search performance.

統計データからの市民の属性復元のための進化計算とSAによる2段階最適化

統計データからの市民の属性復元のための進化計算とSAによる2段階最適化

A transportation network model with intelligent probabilistic travel times and two hybrid algorithms

A main component of a transportation network is travel time or distance. Due to stochastic events such as accidents and failures in roads, a deterministic estimation of the travel time between two cities or regions is impossible. Also, on special occasions like holidays, with increase in the traveling population in the transportation network, probabilities of occurrence of these events increase. The increase in traveling population has a direct effect on the estimation of travel times and subsequently on the decision-making process. Therefore, provision of an appropriate model for the intelligent probabilistic travel times to distribute the population in a transportation network is a practical necessity. Here, we study a capacitated location-multi allocation-routing problem with intelligent probabilistic travel times. In our study, the concept of intelligent probabilistic travel times incurs two issues: (1) consideration of some random factors in computing the travel times and (2) impact of the traveling population on these random factors simultaneously. Here, we consider three random factors of the time spent in traffic, the number of accidents and the number of road failures. It is assumed that server nodes and arcs have limited capacities for accepting the population. After proposing a function for computing the intelligent probabilistic travel time, we first formulate the problem as a mixed-integer nonlinear programming model, and then suitably transform it into a mixed-integer linear programming one. Our aim is to determine appropriate server locations among the candidate locations, allocate the existing population in each demand node to server locations, and find the movement path of each member to reach its corresponding server with respect to the simultaneous change of the probabilistic travel times so that the expected total transportation time is minimized. For small problems, the model is efficiently solvable by the CPLEX software package. For large problems, two solution approaches, a heuristic algorithm incorporating genetic algorithm and local search, and an evolutionary simulated annealing algorithm, are proposed. Comparative numerical results demonstrate the effectiveness of the proposed probabilistic model and the proposed algorithms.

A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy

A real-world newspaper distribution problem with recycling policy is tackled in this work. In order to meet all the complex restrictions contained in such a problem, it has been modeled as a rich vehicle routing problem, which can be more specifically considered as an asymmetric and clustered vehicle routing problem with simultaneous pickup and deliveries, variable costs and forbidden paths (AC-VRP-SPDVCFP). This is the first study of such a problem in the literature. For this reason, a benchmark composed by 15 instances has been also proposed. In the design of this benchmark, real geographical positions have been used, located in the province of Bizkaia, Spain. For the proper treatment of this AC-VRP-SPDVCFP, a discrete firefly algorithm (DFA) has been developed. This application is the first application of the firefly algorithm to any rich vehicle routing problem. To prove that the proposed DFA is a promising technique, its performance has been compared with two other well-known techniques: an evolutionary algorithm and an evolutionary simulated annealing. Our results have shown that the DFA has outperformed these two classic meta-heuristics.

A stochastic user equilibrium assignment problem in discrete network design problem

We formulate the transportation discrete network design problem (DNDP) as a mixed-integer bi-level mathematical problem, based on the concept reserve capacity. The upper level goal programme maximises the reserve capacity by designing the direction of street and increasing the street capacity through lane addition. The lower level problem is stochastic user equilibrium traffic assignment problem within a probit-based path choice decision framework which generates user equilibrium flow patterns. Because of non-convexity nature of the model, meta-heuristic methods used to solve the problem and we used Monte Carlo simulation approach to compute path choice probabilities and stochastic user equilibrium solved by method of successive averages. A hybrid genetic algorithm with simulated annealing and an evolutionary simulated annealing algorithm are proposed. Numerical examples are presented to verify the proposed model and algorithm.

An improved discrete bat algorithm for symmetric and asymmetric Traveling Salesman Problems

Bat algorithm is a population metaheuristic proposed in 2010 which is based on the echolocation or bio-sonar characteristics of microbats. Since its first implementation, the bat algorithm has been used in a wide range of fields. In this paper, we present a discrete version of the bat algorithm to solve the well-known symmetric and asymmetric Traveling Salesman Problems. In addition, we propose an improvement in the basic structure of the classic bat algorithm. To prove that our proposal is a promising approximation method, we have compared its performance in 37 instances with the results obtained by five different techniques: evolutionary simulated annealing, genetic algorithm, an island based distributed genetic algorithm, a discrete firefly algorithm and an imperialist competitive algorithm. In order to obtain fair and rigorous comparisons, we have conducted three different statistical tests along the paper: the Student׳s t-test, the Holm׳s test, and the Friedman test. We have also compared the convergence behavior shown by our proposal with the ones shown by the evolutionary simulated annealing, and the discrete firefly algorithm. The experimentation carried out in this study has shown that the presented improved bat algorithm outperforms significantly all the other alternatives in most of the cases.

A new forecasting framework for volatile behavior in net electricity consumption: A case study in Turkey

Electricity is a significant form of energy that cannot be stored physically and is usually generated as needed. In most research studies, the main aim is to ensure that sufficient electricity is generated to meet future needs. In order to avoid waste or shortage, a good system needs to be designed to constantly maintain the level of electricity needed. It is necessary to estimate independent factors because future electricity volume is based not only on current net consumption but also on independent factors. In this paper, a new framework is proposed to first estimate future independent factors using SARIMA (seasonal auto-regressive iterative moving average) method and NARANN (nonlinear autoregressive artificial neural network) method, both of which are called a ”forecasted scenario approach”. Subsequently, based on these scenarios, a LADES (LASSO-based adaptive evolutionary simulated annealing) model and a RADES (ridge-based adaptive evolutionary simulated annealing) model are applied to forecast the future NEC (net electricity consumption). The proposed approaches are then validated with a case study in Turkey. The experimental results show that our approach outperforms others when compared to previous approaches. Finally, the results show that the NEC can be modeled, and it can be used to predict the future NEC.

An Integrated Qualitative and Quantitative Biochemical Model Learning Framework Using Evolutionary Strategy and Simulated Annealing.

Both qualitative and quantitative model learning frameworks for biochemical systems have been studied in computational systems biology. In this research, after introducing two forms of pre-defined component patterns to represent biochemical models, we propose an integrative qualitative and quantitative modelling framework for inferring biochemical systems. In the proposed framework, interactions between reactants in the candidate models for a target biochemical system are evolved and eventually identified by the application of a qualitative model learning approach with an evolution strategy. Kinetic rates of the models generated from qualitative model learning are then further optimised by employing a quantitative approach with simulated annealing. Experimental results indicate that our proposed integrative framework is feasible to learn the relationships between biochemical reactants qualitatively and to make the model replicate the behaviours of the target system by optimising the kinetic rates quantitatively. Moreover, potential reactants of a target biochemical system can be discovered by hypothesising complex reactants in the synthetic models. Based on the biochemical models learned from the proposed framework, biologists can further perform experimental study in wet laboratory. In this way, natural biochemical systems can be better understood.

Hierarchical Optimization-Based Approach for Two-Dimensional Rectangular Layout Design Problems

In this study, we propose a hierarchical optimization-based approach for two-dimensional rectangular layout design problems. Decomposition-based optimization has been a key approach for complicated design problems in multidisciplinary design optimization (MDO), but the main focus has been design problems where the design variables are continuous. On the other hand, various approaches have been developed for layout design based on evolutionary algorithms, e.g., simulated annealing (SA) and genetic algorithms (GAs) which can handle its combinatorial nature in an effective manner. In the present study, we aim to introduce a new paradigm by combining decomposition-based optimization and evolutionary algorithms for solving complicated layout design problems. In this approach, the original layout problem is decomposed into the top-level layout problem and a set of sublevel layout problems, where the layouts obtained from the sublevel problems are used as components of the top-level problem. Since the preferable shapes of these components are unclear when the sublevel problems are solved, a set of Pareto optima are provided in the sublevel problems and these solutions are used as candidate components in the top-level problem. A computational design algorithm is developed based on this approach, which represents the layout topology with sequence pair and the shape of each subsystem or component with the aspect ratio, and they are optimized using GAs. The Pareto optimality of the sublevels is handled by multi-objective GAs, and a set of Pareto optima is generated simultaneously. The top-level and sublevel layout problems are coordinated via the exchange of preferable ranges for the shapes and layout. This approach was implemented and applied to an example problem to demonstrate its performance and capability.

A medical diagnostic tool based on radial basis function classifiers and evolutionary simulated annealing

ObjectiveThe profusion of data accumulating in the form of medical records could be of great help for developing medical decision support systems. The objective of this paper is to present a methodology for designing data-driven medical diagnostic tools, based on neural network classifiers. MethodsThe proposed approach adopts the radial basis function (RBF) neural network architecture and the non-symmetric fuzzy means (NSFM) training algorithm, which presents certain advantages including better approximation capabilities and shorter computational times. The novelty in this work consists of adapting the NSFM algorithm to train RBF classifiers, and suitably tailoring the evolutionary simulated annealing (ESA) technique to optimize the produced RBF models. The integration of ESA is critical as it helps the optimization procedure to escape from local minima, which could arise from the application of the traditional simulated annealing algorithm, and thus discover improved solutions. The resulting method is evaluated in nine different medical benchmark datasets, where the common objective is to train a suitable classifier. The evaluation includes a comparison with two different schemes for training classifiers, including a standard RBF training technique and support vector machines (SVMs). Accuracy% and the Matthews Correlation Coefficient (MCC) are used for comparing the performance of the three classifiers. ResultsResults show that the use of ESA helps to greatly improve the performance of the NSFM algorithm and provide satisfactory classification accuracy. In almost all benchmark datasets, the best solution found by the ESA-NSFM algorithm outperforms the results produced by the SFM algorithm and SVMs, considering either the accuracy% or the MCC criterion. Furthermore, in the majority of datasets, the average solution of the ESA-NSFM population is statistically significantly higher in terms of accuracy% and MCC at the 95% confidence level, compared to the global optimum solution that its rivals could achieve. As far as computational times are concerned, the proposed approach was found to be faster compared to SVMs. ConclusionsThe results of this study suggest that the ESA-NSFM algorithm can form the basis of a generic method for knowledge extraction from data originating from different kinds of medical records. Testing the proposed approach on a number of benchmark datasets, indicates that it provides increased diagnostic accuracy in comparison with two different classifier training methods.

$$K$$ -page crossing number minimization problem: An evaluation of heuristics and its solution using GESAKP

The \(K\)-page crossing number minimization problem (KPMP) is to determine the minimum number of edge crossings over all \(K\)-page book drawings of a graph \(G\) with the vertices placed in a sequence along the spine and the edges on the \(K\)-pages of the book. In this paper we have (a) statistically evaluated five heuristics for ordering vertices on the spine for minimum number of edge crossings with all the edges placed on a single page, (b) statistically evaluated five heuristics for distributing edges on \(K\)-pages with minimum number of crossings for a fixed ordering of vertices on the spine and (c) implemented and experimentally evaluated an instance of guided evolutionary simulated annealing (GESA) called GESAKP here for solving the KPMP. In accordance with the results of (a) and (b) above, in GESAKP, placement of vertices on the spine is decided using a random depth first search of the graph and an edge embedding heuristic is used to distribute the edges on \(K\)-pages of a book. Extensive experiments have been carried out on a suite of benchmark, standard and random graphs to compare the performance of GESAKP with variants of the simple genetic algorithm and other existing approaches. In order to improve the results for some graphs, simple extensions to GESAKP were made. Experiments show that in almost all cases, zero or low cost could be achieved for \(K\le 5\). Also, for \(K \le \) ‘known upper bound’ i.e. upper bound for minimum number of pages necessary to draw or embed the edges of a graph without crossings, zero crossings were obtained. In general, GESAKP outperformed the other techniques. From our experimental results we also present the conjectures for the \(K\)-page crossing number of some complete tripartite graphs and pagenumber of toroidal meshes and a class of complete bipartite and tripartite graphs.

Multi-objective discrete urban road network design

This paper addresses the problem of designing urban road networks in a multi-objective decision making framework. Given a base network with only two-way links, and the candidate lane addition and link construction projects, the problem is to find the optimal combination of one-way and two-way links, the optimal selection of network capacity expansion projects, and the optimal lane allocations on two-way links to optimize the reserve capacity of the network, and two new travel time related performance measures. The problem is considered in two variations; in the first scenario, two-way links may have different numbers of lanes in each direction and in the second scenario, two-way links must have equal number of lanes in each direction. The proposed variations are formulated as mixed-integer programming problems with equilibrium constraints. A hybrid genetic algorithm, an evolutionary simulated annealing, and a hybrid artificial bee colony algorithm are proposed to solve these two new problems. A new measure is also proposed to evaluate the effectiveness of the three algorithms. Computational results for both problems are presented.

A hybrid meta-heuristic for multi-objective vehicle routing problems with time windows

The Capacitated Vehicle Routing Problem with Time Windows is an important combinatorial optimization problem consisting in the determination of the set of routes of minimum distance to deliver goods, using a fleet of identical vehicles with restricted capacity, so that vehicles must visit customers within a time frame. A large number of algorithms have been proposed to solve single-objective formulations of this problem, including meta-heuristic approaches, which provide high quality solutions in reasonable runtimes. Nevertheless, in recent years some authors have analyzed multi-objective variants that consider additional objectives to the distance travelled. This paper considers not only the minimum distance required to deliver goods, but also the workload imbalance in terms of the distances travelled by the used vehicles and their loads. Thus, MMOEASA, a Pareto-based hybrid algorithm that combines evolutionary computation and simulated annealing, is here proposed and analyzed for solving these multi-objective formulations of the VRPTW. The results obtained when solving a subset of Solomon’s benchmark problems show the good performance of this hybrid approach.

On the Investigation of Nonlinear Dynamics of a Rotor with Rub-impact Using Numerical Analysis and Evolutionary Algorithms

In this paper the dynamics of a rotor-stator system with mass imbalance induced rub-impact interactions is investigated with particular attention on the routes to chaos. The rub-impact interaction is modelled by a Hertz contact radial force and a Coulomb friction tangential force. Extensive numerical experimentation for a wide range of parameters shows the resulting response to be rich in subharmonic, quasiperiodic and chaotic motions. Parameter identification of chaotic systems has become an important topic of research in the past decade. Of particular interest is the problem of identifying or estimating system parameters when the quasiperiodic or chaotic responses of the system are known. The problem of identifying parameters can be cast as an optimization problem and non-traditional optimization methods such as evolutionary algorithms, simulated annealing and others have been developed to identify system parameters. In this paper, three evolutionary algorithms – particle swarm optimization, differential evolution and firefly algorithm are presented and compared for the problem of identifying parameters of a rotordynamical system given a chaotic response. The results of this analysis can potentially be of a considerable value as diagnostic tools in assessing condition monitoring signals that are routinely taken on modern rotating machinery.

A simulated rebounding algorithm applied to the multi-stage security-constrained transmission expansion planning in power systems

This research discusses the multi-stage security-constrained transmission network expansion planning. In modern power systems, the problem is formulated as a large-scale, mixed-integer, non-linear programming problem, which for a real power systems is very difficult to solve. Although remarkable advances have been made in optimization techniques, finding an optimal solution to a problem of this nature can still be extremely challenging. In this paper, a new constructive heuristic approach, based on a local controlled random search (simulated rebounding algorithm) is proposed to choose the decision variables. The model can produce better solutions than other references techniques such as particle swarm optimization, evolutionary particle swarm optimization, genetic algorithms, and simulated annealing algorithm, among other evolutionary methods. The methodology is applied to assess the capabilities of the proposed approach in the Ecuadorian and Chilean Power Systems as an example of application. Simulation results show that the proposed approach is accurate and very efficient, and it has the potential to be applied to real power system planning problems. The algorithm has been presented and applied to the multi-stage security-constrained transmission expansion planning.

Hybrid Metaheuristics Based on Evolutionary Algorithms and Simulated Annealing: Taxonomy, Comparison, and Synergy Test

The design of hybrid metaheuristics with ideas taken from the simulated annealing and evolutionary algorithms fields is a fruitful research line. In this paper, we first present an overview of the hybrid metaheuristics based on simulated annealing and evolutionary algorithms presented in the literature and classify them according to two well-known taxonomies of hybrid methods. Second, we perform an empirical study comparing the behavior of a representative set of the hybrid approaches based on evolutionary algorithms and simulated annealing found in the literature. In addition, a study of the synergy relationships provided by these hybrid approaches is presented. Finally, we analyze the behavior of the best performing hybrid metaheuristic with regard to several state-of-the-art evolutionary algorithms for binary combinatorial problems. The experimental studies presented provide useful conclusions about the schemes for combining ideas from simulated annealing and evolutionary algorithms that may improve the performance of these kinds of approaches and suggest that these hybrids metaheuristics represent a competitive alternative for binary combinatorial problems.

In silico strain optimization by adding reactions to metabolic models

Summary Nowadays, the concerns about the environment and the needs to increase the productivity at low costs, demand for the search of new ways to produce compounds with industrial interest. Based on the increasing knowledge of biological processes, through genome sequencing projects, and high-throughput experimental techniques as well as the available computational tools, the use of microorganisms has been considered as an approach to produce desirable compounds. However, this usually requires to manipulate these organisms by genetic engineering and/ or changing the enviromental conditions to make the production of these compounds possible. In many cases, it is necessary to enrich the genetic material of those microbes with hereologous pathways from other species and consequently adding the potential to produce novel compounds.This paper introduces a new plug-in for the OptFlux Metabolic Engineering platform, aimed at finding suitable sets of reactions to add to the genomes of selected microbes (wild type strain), as well as finding complementary sets of deletions, so that the mutant becomes able to overproduce compounds with industrial interest, while preserving their viability. The necessity of adding reactions to the metabolic model arises from existing gaps in the original model or motivated by the productions of new compounds by the organism. The optimization methods used are metaheuristics such as Evolutionary Algorithms and Simulated Annealing. The usefulness of this plug-in is demonstrated by a case study, regarding the production of vanillin by the bacterium E. coli.

Black-box complexities of combinatorial problems

Black-box complexity, counting the number of queries needed to find the optimum of a problem without having access to an explicit problem description, was introduced by Droste, Jansen, and Wegener [S. Droste, T. Jansen, I. Wegener, Upper and lower bounds for randomized search heuristics in black-box optimization, Theory of Computing Systems 39 (2006) 525–544] to measure the difficulty of solving an optimization problem via generic search heuristics such as evolutionary algorithms, simulated annealing or ant colony optimization.Since then, a number of similar complexity notions were introduced. However, so far these new notions were only analyzed for artificial test problems. In this paper, we move a step forward and analyze the different black-box complexity notions for two classic combinatorial problems, namely the minimum spanning tree and the single-source shortest path problem. Besides proving bounds for their black-box complexities, our work reveals that the choice of how to model the optimization problem has a significant influence on its black-box complexity. In addition, when regarding the unbiased (symmetry-invariant) black-box complexity of combinatorial problems, it is important to choose a meaningful definition of unbiasedness.

A modified colonial competitive algorithm for the mixed-model U-line balancing and sequencing problem

Implementation of mixed-model U-shaped assembly lines (MMUL) is emerging and thriving in modern manufacturing systems owing to adaptation to changes in market demand and application of just-in-time production principles. In this study, the line balancing and model sequencing (MS) problems in MMUL are considered simultaneously, which results in the NP-hard mixed-model U-line balancing and sequencing (MMUL/BS) problem. A colonial competitive algorithm (CCA) is developed and modified to solve the MMUL/BS problem. The modified CCA (MCCA) improves performance of original CCA by introducing a third type of country, independent country, to the population of countries maintained by CCA. Implementation details of the proposed CCA and MCCA are elaborated using an illustrative example. Performance of the proposed algorithms is tested on a set of test-bed problems and compared with that of existing algorithms such as co-evolutionary algorithm, endosymbiotic evolutionary algorithm, simulated annealing, and genetic algorithm. Computational results and comparisons show that the proposed algorithms can improve the results obtained by existing algorithms developed for MMUL/BS.