Simulated Evolution Algorithm Research Articles

Synchronization of non-smooth chaotic systems via an improved reservoir computing

The reservoir computing (RC) is increasingly used to learn the synchronization behavior of chaotic systems as well as the dynamical behavior of complex systems, but it is scarcely applied in studying synchronization of non-smooth chaotic systems likely due to its complexity leading to the unimpressive effect. Here proposes a simulated annealing-based differential evolution (SADE) algorithm for the optimal parameter selection in the reservoir, and constructs an improved RC model for synchronization, which can work well not only for non-smooth chaotic systems but for smooth ones. Extensive simulations show that the trained RC model with optimal parameters has far longer prediction time than those with empirical and random parameters. More importantly, the well-trained RC system can be well synchronized to its original chaotic system as well as its replicate RC system via one shared signal, whereas the traditional RC system with empirical or random parameters fails for some chaotic systems, particularly for some non-smooth chaotic systems.

Ant-Colony-Algorithm-Based Intelligent Transmission Network Planning

Background: The efficiency of wireless sensor networks is limited by limitations in energy supply. Efficient routing strategies should be designed to save and balance the energy consumption of each node in a wireless sensor network. Aim: In this study, a transmission network based on an ant colony algorithm was proposed to meet the power load demands of a city. Objective: Based on the chaos ant colony algorithm, using a combination of wireless sensor network and node residual energy factors, a neighbor selection strategy was proposed. Results: The optimal result was 1896, and additional lines were: N14-15= 2, N4-16= 1, N5-12= 2, N7-13= L, N6-14= 1, N7-8. The coding method of solving transmission network planning based on multi-stage and multi-dimensional control variables was employed to decompose each control variable into two variables. The sum of total weight and non-zero bits was transformed into high-dimensional variables in state transition probability. Conclusion: The key analysis showed that the ant colony algorithm, as a simulated evolutionary algorithm, is an efficient internal heuristic method.

Temporal Design of Art Painting Landscape Based on Ant Colony Optimization Algorithm

In the long history of painting, art research in each period will also reflect on the current situation and future improvement. Inspired by the group behavior of natural insect communities, to find the shortest path between insect nests and food, a simulated evolutionary algorithm ant colony algorithm is proposed to simulate the foraging behavior of natural ant colonies. This paper studies the temporal design based on ant colony optimization algorithm. Time is an inseparable part of landscape design. With the continuous progress of time, various landscape characteristics have also been formed. The purpose of this paper is to provide theoretical and practical support for the improvement of art painting landscape design from the perspective of time. The ant colony optimization algorithm is used to analyze and analyze the values of the algorithm parameters.

Study on the random walk classification algorithm of polyant colony

Abstract With the sustained and healthy development of economy, saving energy and reducing consumption and improving energy utilization rate is a major task that enterprises need to solve. With the complex and large-scale chemical process, the heat exchange network has become complex and diverse. For more and more complex and large-scale industrial heat exchange networks, there are many different kinds of heat exchangers, the flow is complex, so the heat exchange network presents a high degree of complexity, a node status change; its disturbance transfer will influence the stability of other nodes associated with it, because of the system coupling, thus affecting the controllability and reliability of the whole heat exchanger network. Process optimization design of heat exchange network is one of the main methods of energy saving in the industrial field. As a typical simulated evolutionary algorithm in swarm intelligence algorithm, ant colony algorithm combined with random walk classification algorithm, this article proposes an optimized heat transfer network based on multi-ant colony random walk classification algorithm. The heat exchanger was abstracted as a node, and the heat exchanger pipeline was abstracted as a side. According to the maximum geometric multiplicity of the eigenvalue of the adjacency matrix and the linear correlation row vector of the matrix, and combining the importance of the edge of the heat exchange network with the controllable range of the driving edge, the optimal control driving edge of the heat exchange network is identified. The results show that compared with the traditional heat exchanger, the size of the enhanced heat transfer equipment and the influence of pressure drop change. Compared with the results of the size of the heat exchanger strengthening heat transfer equipment and the stepwise optimization of the heat exchange network in this study, the cost of public engineering is reduced by 5.98% and the total cost is reduced by 8.83%.

Dataflow-Aware Macro Placement Based on Simulated Evolution Algorithm for Mixed-Size Designs

This article proposes a novel approach to handle macro placement. Previous works usually apply the simulated annealing (SA) algorithm to handle this problem. However, the SA-based approaches usually have difficulty in handling preplaced macros and require longer runtime. To resolve these problems, we propose a macro placement procedure based on the corner stitching data structure and then apply an efficient and effective simulated evolution algorithm to further refine placement results. In order to relieve local routing congestion, we propose to expand areas of movable macros according to the design hierarchy before applying the macro placement algorithm. Finally, we extend our macro placement methodology to consider dataflow constraint so that dataflow-related macros can be placed at close locations. The experimental results show that our approach obtains a better solution than a previous macro placement algorithm and a tool. Besides, placement quality can be further improved when the dataflow constraint is considered.

Will Skynet Need a Librarian? A Literature Review of Machine LearninggBased Data Organization and Use Strategies.

The increasing use of machine learning in everyday life, from loan applications to morning coffee, has developed alongside the rocketing growth of data professionally, attempting to leave no stone unturned in the quest for new insights, and personally, as digitally-connected people both produce and consume more data than ever before. Learning how machine learning models and algorithms engage with data organization is important in understanding how to improve the utility and benefits of current and future machine learning uses. In surveying the literature landscape, we aim to look at the intersection of machine learning and data management from a few different fields for a comprehensive, albeit brief, view of this fascinating area of modern science and technology. In this literature review, databases, both relational and nonrelational, are covered, along with four types of machine learning: symbolic machine learning, neural networks, simulated evolution and genetic algorithms, and particle swarm optimization. We’ll also take a look at machine learning-focused information retrieval strategies and how selected fields utilize and advance machine learning.

Parameter identification based on modified simulated annealing differential evolution algorithm for giant magnetostrictive actuator

There is a serious nonlinear relationship between input and output in the giant magnetostrictive actuator (GMA) and how to establish mathematical model and identify its parameters is very important to study characteristics and improve control accuracy. The current-displacement model is firstly built based on Jiles-Atherton (J-A) model theory, Ampere loop theorem and stress-magnetism coupling model. And then laws between unknown parameters and hysteresis loops are studied to determine the data-taking scope. The modified simulated annealing differential evolution algorithm (MSADEA) is proposed by taking full advantage of differential evolution algorithm’s fast convergence and simulated annealing algorithm’s jumping property to enhance the convergence speed and performance. Simulation and experiment results shows that this algorithm is not only simple and efficient, but also has fast convergence speed and high identification accuracy.

Adaptive Simulated Evolution based Approach for Cluster Optimization in Wireless Sensor Networks

Energy consumption minimization is crucial for the constrained sensors in wireless sensor networks (WSNs). Partitioning WSNs into optimal set of clusters is a promising technique utilized to minimize energy consumption and to increase the lifetime of the network. However, optimizing the network into optimal set of clusters is a non-polynomial (NP) hard problem, and the time needed to solve such problem increases exponentially as the number of sensors increases. In this paper, simulated evolution (SimE) algorithm is engineered to tackle the problem of cluster optimization in WSNs. A goodness measure is developed to measure the accuracy of assigning nodes to clusters and to evaluate the clustering quality of the overall network. SimE was developed such that the number of clusters and cluster heads are adaptive to number of alive nodes in the network. In fact, extensive simulation results demonstrate that SimE provides near optimal clustering and improves the lifetime of the network by about 21% compared to the traditional LEACH-C protocol.

A multi-objective evolutionary artificial bee colony algorithm for optimizing network topology design

Abstract The topological design of a computer communication network is a well-known NP-hard problem. The problem complexity is further magnified by the presence of multiple design objectives and numerous design constraints. This paper presents a goal programming-based multi-objective artificial bee colony optimization (MOABC) algorithm to solve the problem of topological design of distributed local area networks (DLANs). Five design objectives are considered herein, namely, network reliability, network availability, average link utilization, monetary cost, and network delay. Goal programming (GP) is incorporated to aggregate the multiple design objectives into a single objective function. A modified version of MOABC, named as evolutionary multi-objective ABC (EMOABC) is also proposed which incorporates the characteristics of simulated evolution (SE) algorithm for improved local search. The effect of control parameters of MOABC is investigated. Comparison of EMOABC with MOABC and the standard ABC (SABC) shows better performance of EMOABC. Furthermore, a comparative analysis is also done with non-dominated sorting genetic algorithm II (NSGA-II), Pareto-dominance particle swarm optimization (PDPSO) algorithm and two recent variants of decomposition based multi-objective evolutionary algorithms, namely, MOEA/D-1 and MOEA/D-2. Results indicate that EMOABC demonstrated superior performance than all the other algorithms.

A resource allocation evolutionary algorithm for OFDM system

Resource allocation for orthogonal frequency division multiplexing OFDM system, as a core technology for the 4th generation mobile communication system, has shown significant importance in the improvement of system transmission rate. At present, the two-step algorithm is described as a main method to deal with resource allocation for OFDM system. As carrier allocation and power allocation are not independent of each other, the two-step algorithm may result in lower complexity but poor convergence. This paper proposes a mixed evolutionary algorithm to allocate sub-carrier and power at the same time. It combines the simulated annealing algorithm and evolutionary algorithm. This paper also improves the definition of fairness in OFDM system. It can achieves perfect fairness among users while maintaining the efficient capacity performance. Simulation results demonstrate that the proposed resource allocation algorithm can achieve perfect performance, and increase the convergence rate greatly meanwhile efficiently improving the system capacity.

Fuzzy particle swarm optimization algorithms for the open shortest path first weight setting problem

The open shortest path first (OSPF) routing protocol is a well-known approach for routing packets from a source node to a destination node. The protocol assigns weights (or costs) to the links of a network. These weights are used to determine the shortest paths between all sources to all destination nodes. Assignment of these weights to the links is classified as an NP-hard problem. The aim behind the solution to the OSPF weight setting problem is to obtain optimized routing paths to enhance the utilization of the network. This paper formulates the above problem as a multi-objective optimization problem. The optimization metrics are maximum utilization, number of congested links, and number of unused links. These metrics are conflicting in nature, which motivates the use of fuzzy logic to be employed as a tool to aggregate these metrics into a scalar cost function. This scalar cost function is then optimized using a fuzzy particle swarm optimization (FPSO) algorithm developed in this paper. A modified variant of the proposed PSO, namely, fuzzy evolutionary PSO (FEPSO), is also developed. FEPSO incorporates the characteristics of the simulated evolution heuristic into FPSO. Experimentation is done using 12 test cases reported in literature. These test cases consist of 50 and 100 nodes, with the number of arcs ranging from 148 to 503. Empirical results have been obtained and analyzed for different values of FPSO parameters. Results also suggest that FEPSO outperformed FPSO in terms of quality of solution by achieving improvements between 7 and 31 %. Furthermore, comparison of FEPSO with various other algorithms such as Pareto-dominance PSO, weighted aggregation PSO, NSGA-II, simulated evolution, and simulated annealing algorithms revealed that FEPSO performed better than all of them by achieving best results for two or all three objectives.

I-Ching Divination Evolutionary Algorithm and its Convergence Analysis.

An innovative simulated evolutionary algorithm (EA), called I-Ching divination EA (IDEA), and its convergence analysis are proposed and investigated in this paper. Inherited from ancient Chinese culture, I-Ching divination has always been used as a divination system in traditional and modern China. There are three operators evolved from I-Ching transformations in this new optimization algorithm, intrication operator, turnover operator, and mutual operator. These new operators are very flexible in the evolution procedure. Additionally, two new spaces are defined in this paper, which are denoted as hexagram space and state space. In order to analyze the convergence property of I-Ching divination algorithm, Markov model was adopted to analyze the characters of the operators. Meanwhile, the proposed algorithm is proved to be a homogeneous Markov chain with the positive transition matrix. After giving some basic concepts of necessary theorems, definition of admissible functions and I-Ching map, a precise proof of the states converge to the global optimum is presented. Compared with the genetic algorithm, particle swarm optimization, and differential evolution algorithm, our proposed IDEA is much faster in reaching the global optimum.

Multi-objective homecare worker scheduling: A fuzzy simulated evolution algorithm approach

As the need for improved homecare services continues to rise in every society, homecare service providers need to develop efficient staff scheduling methods. Homecare services are aimed at providing medical, paramedical, and social aid to patients at their own homes, leading to reduced hospitalization and healthcare operations costs in the medium to long term. However, the home healthcare worker scheduling problem is a complex one since it combines the hard vehicle routing and the staff assignment problems. As such, the purpose of this research is to present a fuzzy simulated evolution algorithm, based on fuzzy evaluation techniques, to address the homecare worker scheduling problem in a home care environment. The main objective is to decide on (i) which patients to assign to each worker, and (ii) the best route or trip for each care giver to execute the healthcare tasks so as to satisfy the patient's preferences in the time window during which the care giver should visit and offer the requested healthcare service. Computational results on illustrative experiments show that the proposed algorithm is a promising decision support tool.

Simulated evolution and simulated annealing algorithms for solving multi-objective open shortest path first weight setting problem

Optimal utilization of resources in present-day communication networks is a challenging task. Routing plays an important role in achieving optimal resource utilization. The open shortest path first (OSPF) routing protocol is widely used for routing packets from a source node to a destination node. This protocol assigns weights (or costs) to the links of a network. These weights are used to determine the shortest path between all sources to all destination nodes. Assignment of these weights to the links is classified as an NP-hard problem. This paper formulates the OSPF weight setting problem as a multi-objective optimization problem, with maximum utilization, number of congested links, and number of unused links as the optimization objectives. Since the objectives are conflicting in nature, an efficient approach is needed to balance the trade-off between these objectives. Fuzzy logic has been shown to efficiently solve multi-objective optimization problems. A fuzzy cost function for the OSPF weight setting problem is developed in this paper based on the Unified And-OR (UAO) operator. Two iterative heuristics, namely, simulated annealing (SA) and simulated evolution (SimE) have been implemented to solve the multi-objective OSPF weight setting problem using a fuzzy cost function. Results are compared with that found using other cost functions proposed in the literature (Sqalli et al. in Network Operations and Management Symposium, NOMS, 2006). Results suggest that, overall, the fuzzy cost function performs better than existing cost functions, with respect to both SA and SimE. Furthermore, SimE shows superior performance compared to SA. In addition, a comparison of SimE with NSGA-II shows that, overall, SimE demonstrates slightly better performance in terms of quality of solutions.

Ranking optimization approach enhancement of line-up algorithm

Ranking is a technique to categorize & finding the best option in the market. When number of best option is available in the market so its difficult to getting the best option is always a problem. In this paper we proposed a technique to optimize the ranking and its availability to check performance factor in order to maintained high ranking and quality of popular option in the market. We enhanced the line-up algorithm for ranking optimization approached, so, we used to line-up technique is demonstration to check other factor which affect to ranking of products, we are finding research to get factor detail which to improve the ranking of product. I. Introduction The line-up algorithm is a simulated evolutionary algorithm, is proposed for solving combinative expansion problems. In Line-up algorithm is exist independent and parallel evolution. The competitions between tribes and family are used to increase the search efficiency. All clan are ranked a line-up based on their main function and allocated different search spaces according to their position in the line-up, which is favourable to balance local and global search. The Line-up algorithm is applied to the solution of knapsack problem and the optimal design of pressure relief header network. Computational results reveal that the Line-up algorithm is able to find optimal or near-optimal solutions after examining an extremely small fraction of search space. This interactive technique that addresses the limitations of existing methods and is motivated by a comprehensive analysis of requirements of multi-attribute rankings considering various domains (1).Based on this analysis, the design and implementation of Line-Up, a visual analysis technique for creating, refining, and exploring rankings based on complex combinations of attributes. We demonstrate the application of Line-Up in two use cases in which we explore and analyze university rankings and nutrition data. We evaluate Line-Up in a qualitative study that demonstrates the utility of approaches. The evaluation shows that users are enable to solve complex ranking tasks in a short period of time. In this paper we introduced Line-Up technique for creating, analyzing, and comparing multiple attribute rankings. Initially, our goal for the technique was to enable domain experts to formulate complex biological questions in an intuitive and visual way. When we realized that the technique can be applied in many scenarios, we chose to generalize it to a domain-independent universal solution for rankings of items. Web page ranking requires the optimization of sophisticated performance measures. ranking is a technique to categorize & finding the best option in the market. When number of best option is available in the market so its difficult to getting the best option is always a problem. We proposed an enhanced to current technique which provide a solution to user to optimize the ranking in order to get a solution to the problem which they face to find the factor form andthey are lagging to maintain to good & high ranking for their product & distribution.

Binary particle swarm optimization (BPSO) based state assignment for area minimization of sequential circuits

State assignment (SA) for finite state machines (FSMs) is one of the main optimization problems in the synthesis of sequential circuits. It determines the complexity of its combinational circuit and thus area, delay, testability and power dissipation of its implementation. Particle swarm optimization (PSO) is a non-deterministic heuristic that optimizes a problem by iteratively trying to improve a candidate solution with regard to a given measure of quality. PSO optimizes a problem by having a population of candidate solutions called particles, and moving them around in the search-space according to a simple mathematical formulae. In this paper, we propose an improved binary particle swarm optimization (BPSO) algorithm and demonstrate its effectiveness in solving the state assignment problem in sequential circuit synthesis targeting area optimization. It will be an evident that the proposed BPSO algorithm overcomes the drawbacks of the original BPSO algorithm. Experimental results demonstrate the effectiveness of the proposed BPSO algorithm in comparison to other BPSO variants reported in the literature and in comparison to Genetic Algorithm (GA), Simulated Evolution (SimE) and deterministic algorithms like Jedi and Nova.

Testing and Analysis of Several Global Optimization Algorithms

Global optimization algorithms have strong adaptability become a major research direction. Three global optimization algorithms which are Multi-Island Genetic Algorithm (MIGA), Adaptive simulated annealing (ASA) and Evolutionary Algorithms (EVOL) are adopted as the optimization policy. Tested with Zakharov function and Rastrigin function to analyze the performance of the global optimization algorithms.According to the results, it can be concluded that one should choose corresponding optimization methods for optimal calculation with specific issues, so that it can obtain the best optimal solution.

Intelligent Optimization Study of Traveling Salesman Problem

Ant colony optimization algorithm is a new simulated evolutionary algorithm by simulating the ant routing behavior of the natural world. In this paper, the basic principle of ant colony algorithm is applied to the classic TSP problem. Simulation examples show that the ant colony algorithm has better robustness and can quickly find the optimal solution.

A fuzzy simulated evolution algorithm for integrated manufacturing system design

Article history: Received September202012 Received in revised format December 28 2012 Accepted January2 2013 Available online 3January 2013 Integrated cell formation and layout (CFLP) is an extended application of the group technology philosophy in which machine cells and cell layout are addressed simultaneously. The aim of this technological innovation is to improve both productivity and flexibility in modern manufacturing industry. However, due to its combinatorial complexity, the cell formation and layout problem is best solved by heuristic and metaheuristic approaches. As CFLP is prevalent in manufacturing industry, developing robust and efficient solution methods for the problem is imperative. This study seeks to develop a fuzzy simulated evolution algorithm (FSEA) that integrates fuzzy-set theoretic concepts and the philosophy of constructive perturbation and evolution. Deriving from the classical simulated evolution algorithm, the search efficiency of the major phases of the algorithm is enhanced, including initialization, evaluation, selection and reconstruction. Illustrative computational experiments based on existing problem instances from the literature demonstrate the utility and the strength of the FSEA algorithm developed in this study. It is anticipated in this study that the application of the algorithm can be extended to other complex combinatorial problems in industry. © 2013 Growing Science Ltd. All rights reserved

Based on the Ant Colony Algorithm TSP Optimization Simulation

Ant colony algorithm ( ant colony algorithm, ACA ), Also known as ant algorithm, is a diagram used to find the probability of path optimization algorithm. Ant colony algorithm is a novel simulated evolutionary algorithm , A preliminary study shows that the algorithm has many good properties. Currently a number of ant colony algorithm has been optimized combination of problems, such as TSP, QAP, JSP, VRP and other issues demonstrated its outstanding prospect. This paper is trying to optimize the existing combination of ant colony algorithm based on the reality of defects, from the pheromone update useless to achieve repetition rate of the control nodes, thereby enhancing the ant colony algorithm in the iterative process of updating optimum capacity. For the same number of iterations to find the path shorter, less costly rules. And in VC++ program to validate the optimal solution of TSP to solve practical problems.