Extremal optimization

About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.

A new ant colony routing approach with a trade-off between system and user optimum

Abstract: Dynamic traffic routing (DTR) refers to the process of (re)directing traffic at junctions in a traffic network corresponding to the evolving traffic conditions as time progresses. This paper considers the DTR problem for a traffic network defined as a directed graph, and deals with the mathematical aspects of the resulting optimization problem from the viewpoint of network flow theory. Traffic networks may have thousands of links and nodes, resulting in a sizable and computationally complex nonlinear, non-convex DTR optimization problem. To solve this problem Ant Colony Optimization (ACO) is chosen as the optimization method in this paper because of its powerful optimization heuristic for combinatorial optimization problems. However, the standard ACO algorithm is not capable of solving the routing optimization problem aimed at the system optimum, and therefore a new ACO algorithm is developed to achieve the goal of finding the optimal distribution of traffic flows in the network.

An ant colony optimization based layout optimization algorithm

Abstract: Ant colony optimization (ACO) has been applied to several NP-hard combinatorial optimization problems with considerable success while little experience is available on continuous constrained optimization problems In this paper ACO was adopted in the solution of a layout optimization problem with dynamic constraints A quasi traveling salesman problem (TSP) model was introduced for local layout improvement by a discretization approach and its ACO based optimization algorithm was then developed The proposed methodology demonstrates its feasibility and validity on a numerical example

Optimal P-Q Control of Grid-Connected Inverters in a Microgrid Based on Adaptive Population Extremal Optimization

Abstract: The optimal P-Q control issue of the active and reactive power for a microgrid in the grid-connected mode has attracted increasing interests recently. In this paper, an optimal active and reactive power control is developed for a three-phase grid-connected inverter in a microgrid by using an adaptive population-based extremal optimization algorithm (APEO). Firstly, the optimal P-Q control issue of grid-connected inverters in a microgrid is formulated as a constrained optimization problem, where six parameters of three decoupled PI controllers are real-coded as the decision variables, and the integral time absolute error (ITAE) between the output and referenced active power and the ITAE between the output and referenced reactive power are weighted as the objective function. Then, an effective and efficient APEO algorithm with an adaptive mutation operation is proposed for solving this constrained optimization problem. The simulation and experiments for a 3 kW three-phase grid-connected inverter under both nominal and variable reference active power values have shown that the proposed APEO-based P-Q control method outperforms the traditional Z-N empirical method, the adaptive genetic algorithm-based, and particle swarm optimization-based P-Q control methods.

Genetic algorithms for solving mixed-discrete optimization problems

Abstract: This paper presents the applications of genetic algorithms to nonlinear constrained mixed-discrete optimization problems that occur in engineering design. Genetic algorithms are heuristic combinatorial optimization strategies. Several strategies are adopted to enhance the search efficiency and reduce the computational cost. The effectiveness, robustness, and fast convergence of modified genetic algorithms are demonstrated through the results of several examples. Moreover, genetic algorithms are more capable of locating the global optimum.