Anti-synchronization of discrete-time chaotic systems using optimization algorithms

In this paper, anti-synchronization of discrete chaotic system based on optimization algorithms are investigated. Different controllers have been used for anti-synchronization of two identical discrete chaotic systems. A proportional-integral-derivative (PID) control is used and its parameters is tuned by the four optimization algorithms, such as genetic algorithm (GA), particle swarm optimization (PSO), modified particle swarm optimization (MPSO) and improved particle swarm optimization (IPSO). Simulation results of these optimization methods to determine the PID controller parameters to anti-synchronization of two chaotic systems are compared. Numerical results show that the improved particle swarm optimization has the best result

A Chaotic Particle Swarm Optimization (CPSO) Algorithm for Solving Optimal Reactive Power Dispatch Problem

This paper presents a chaotic particle swarm algorithm for solving the multi-objective reactive power dispatch problem. To deal with reactive power optimization problem, a chaotic particle swarm optimization (CPSO) is presented to avoid the premature convergence. By fusing with the ergodic and stochastic chaos, the novel algorithm explores the global optimum with the comprehensive learning strategy. The chaotic searching region can be adjusted adaptively.  In order to evaluate the proposed algorithm, it has been tested on IEEE 30 bus system and simulation results show that (CPSO)   is more efficient than other algorithms in reducing the real power loss and maximization of voltage stability index. Keywords:chaotic particle swarm optimization, Optimization, Swarm Intelligence, optimal reactive power, Transmission loss

Chaotic particle swarm optimization

Abstract: A new particle swarm optimization (PSO) algorithm with has a chaotic neural network structure, is proposed. The structure is similar to the Hop¯eld neural network with transient chaos, and has an improved ability to search for globally optimal solution and does not su®er from problems of premature convergence. The presented PSO model is discrete-time discrete-state. The bifurcation diagram of a particle shows that it converges to a stable fixed point from a strange attractor, guaranteeing system convergence

A Novel Chaotic Particle Swarm Optimization Algorithm for Parking Space Guidance

An evolutionary approach of parking space guidance based upon a novel Chaotic Particle Swarm Optimization (CPSO) algorithm is proposed. In the newly proposed CPSO algorithm, the chaotic dynamics is combined into the position updating rules of Particle Swarm Optimization to improve the diversity of solutions and to avoid being trapped in the local optima. This novel approach, that combines the strengths of Particle Swarm Optimization and chaotic dynamics, is then applied into the route optimization (RO) problem of parking lots, which is an important issue in the management systems of large-scale parking lots. It is used to find out the optimized paths between any source and destination nodes in the route network. Route optimization problems based on real parking lots are introduced for analyzing and the effectiveness and practicability of this novel optimization algorithm for parking space guidance have been verified through the application results

Chaotic particle swarm optimization with neural network structure and its application

Abstract: A new particle swarm optimization (PSO) algorithm having a chaotic Hopfield Neural Network (HNN) structure is proposed. Particles exhibit chaotic behaviour before converging to a stable fixed point which is determined by the best points found by the individual particles and the swarm. During the evolutionary process, the chaotic search expands the search space of individual particles. Using a chaotic system to determine particle weights helps the PSO to escape from the local extreme and find the global optimum. The algorithm is applied to some benchmark problems and a pressure vessel problem with nonlinear constraints. The results show that the proposed algorithm consistently outperforms rival algorithms by enhancing search efficiency and improving search qualit

A Chaotic Particle Swarm Optimization-Based Heuristic for Market-Oriented Task-Level Scheduling in Cloud Workflow Systems

Cloud workflow system is a kind of platform service based on cloud computing. It facilitates the automation of workflow applications. Between cloud workflow system and its counterparts, market-oriented business model is one of the most prominent factors. The optimization of task-level scheduling in cloud workflow system is a hot topic. As the scheduling is a NP problem, Ant Colony Optimization (ACO) and Particle Swarm Optimization (PSO) have been proposed to optimize the cost. However, they have the characteristic of premature convergence in optimization process and therefore cannot effectively reduce the cost. To solve these problems, Chaotic Particle Swarm Optimization (CPSO) algorithm with chaotic sequence and adaptive inertia weight factor is applied to present the task-level scheduling. Chaotic sequence with high randomness improves the diversity of solutions, and its regularity assures a good global convergence. Adaptive inertia weight factor depends on the estimate value of cost. It makes the scheduling avoid premature convergence by properly balancing between global and local exploration. The experimental simulation shows that the cost obtained by our scheduling is always lower than the other two representative counterparts

Chaotic Quantum Double Delta Swarm Algorithm using Chebyshev Maps: Theoretical Foundations, Performance Analyses and Convergence Issues

Quantum Double Delta Swarm (QDDS) Algorithm is a new metaheuristic algorithm inspired by the convergence mechanism to the center of potential generated within a single well of a spatially co-located double-delta well setup. It mimics the wave nature of candidate positions in solution spaces and draws upon quantum mechanical interpretations much like other quantum-inspired computational intelligence paradigms. In this work, we introduce a Chebyshev map driven chaotic perturbation in the optimization phase of the algorithm to diversify weights placed on contemporary and historical, socially-optimal agents' solutions. We follow this up with a characterization of solution quality on a suite of 23 single-objective functions and carry out a comparative analysis with eight other related nature-inspired approaches. By comparing solution quality and successful runs over dynamic solution ranges, insights about the nature of convergence are obtained. A two-tailed t-test establishes the statistical significance of the solution data whereas Cohen's d and Hedge's g values provide a measure of effect sizes. We trace the trajectory of the fittest pseudo-agent over all function evaluations to comment on the dynamics of the system and prove that the proposed algorithm is theoretically globally convergent under the assumptions adopted for proofs of other closely-related random search algorithms.Comment: 27 pages, 4 figures, 19 table