Extremal optimization

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

Island-Model-Based Distributed Modified Extremal Optimization for Reducing Crossovers in Reconciliation Graph

Abstract: To determine the mechanism of molecular evolution, molecular biologists need to carry out reconciliation work In reconciliation work, they compare the relation between two heterogeneous phylogenetic trees and the relation between a phylogenetic tree and a taxonomic tree Phylogenetic trees and taxonomic trees are referred to as ordered trees and a reconciliation graph is constructed from two ordered trees In the reconciliation graph, the leaf nodes of the two ordered trees face each other Furthermore, leaf nodes with the same label name are connected to each other by an edge To perform reconciliation work efficiently, it is necessary to find the state with the minimum number of crossovers of edges between leaf nodes Reducing crossovers in a reconciliation graph is the combinatorial optimization problem that finds the state with the minimum number of crossovers In this chapter, a novel bio-inspired heuristic called island-model-based distributed modified extremal optimization (IDMEO) is presented This heuristic is a hybrid of population-based modified extremal optimization (PMEO) and the distributed genetic algorithm using the island model that is used for reducing crossovers in a reconciliation graph

Solving the Maximum Satisfiability Problem Using an Evolutionary Local Search Algorithm

Abstract: The MAXimum propositional SATisfiability problem (MAXSAT) is a well known NP-hard optimization problem with many theoretical and practical applications in artificial intelligence and mathematical logic. Heuristic local search algorithms are widely recognized as the most effective approaches used to solve them. However, their performance depends both on their complexity and their tuning parameters which are controlled experimentally and remain a difficult task. Extremal Optimization (EO) is one of the simplest heuristic methods with only one free parameter, which has proved competitive with the more elaborate general-purpose method on graph partitioning and coloring. It is inspired by the dynamics of physical systems with emergent complexity and their ability to self-organize to reach an optimal adaptation state. In this paper, we propose an extremal optimization procedure for MAXSAT and consider its effectiveness by computational experiments on a benchmark of random instances. Comparative tests showed that this procedure improves significantly previous results obtained on the same benchmark with other modern local search methods like WSAT, simulated annealing and Tabu Search (TS).

Application of hybrid heuristic optimization algorithms for solving optimal hydrothermal system operation

Abstract: This paper provides a thorough comparative assessment of the capabilities of three hybrid metaheuristic algorithms for solving the optimal hydrothermal system operation (OHSO) problem. Among the selected algorithms are Differential Evolution with Adaptive Crossover Operator (DE-ACO), Linearized Biogeography-based Optimization (LBBO), and Hybrid Median-Variance Mapping Optimization (MVMO-SH). Numerical tests are performed on a benchmark system composed by four cascaded hydro plants and an equivalent thermal plant. Performance comparisons include convergence speed, achieved optimum solutions, computing effort, and closeness with results obtained through classical non-linear programming optimization.

Solving TSP Using Improved Elitist Ant System Based on Improved Pheromone Strategy and Dynamic Candidate List

Abstract: Ant colony optimization is a technique for optimization that was introduced by Marco Dorigo in the early 1990’s. The inspiring source of ant colony optimization is the foraging behaviour of real ant colonies. The ant system is a new meta-heuristic for hard combinatorial optimization problems. It is a population-based approach that uses exploitation of positive feedback as well as greedy search. TSP is one of the most famous NP HARD combinatorial optimization (CO) problems and which has wide application background. Combinatorial optimization (CO) is a topic that consist of finding an optimal object from a set of object. Ant colony optimization which has been proven a successful technique and applied to a number of combinatorial optimization problems and taken as one of the high performance computing methods for travelling salesman problem (TSP). But ACO algorithm costs too much time to convergence and traps in local optima in order to find an optimal solution for TSP problems. In this paper we propose an improved ant colony optimization algorithm with two highlights. First, candidate set strategy is adapted to rapid convergence speed. Second, the adaptive adjustment pheromone strategy is used to make relatively uniform pheromone distribution to balance the exploration and exploitation between the random search of ant.