Extremal optimization

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

Comparing extremal and thermal explorations of energy landscapes

Abstract: Using a non-thermal local search, called Extremal Optimization (EO), in conjunction with a recently developed scheme for classifying the valley structure of complex systems, we analyze a short-range spin glass. In comparison with earlier studies using a thermal algorithm with detailed balance, we determine which features of the landscape are algorithm dependent and which are inherently geometrical. Apparently a characteristic for any local search in complex energy landscapes, the time series of successive energy records found by EO is also characterized approximately by a Poisson statistic with logarithmic time arguments. Differences in the results provide additional insights into the performance of EO. In contrast with a thermal search, the extremal search visits dramatically higher energies while returning to more widely separated low-energy configurations. Two important properties of the energy landscape are independent of either algorithm: first, to find lower energy records, progressively higher energy barriers need to be overcome. Second, the Hamming distance between two consecutive low-energy records is linearly related to the height of the intervening barrier.

Exploration-exploitation tradeoffs in metaheuristics: Survey and analysis

Abstract: Metaheuristics (MHs) have been established as a family of the most practical approaches to hard optimization problems. Metaheuristic (MH) algorithm is a high-level problem-independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms. Many different kinds of MHs (e.g. genetic algorithms, tabu search, simulated annealing etc) were proposed during last several decades. Most of MHs focused on experimental studies and applications. It is well known that a suitable and reasonable tradeoff between exploration and exploitation (T: Er& Ei) is crucial for their success, and having a great effect on global optimization performance, e.g., accuracy and convergence speed of those algorithms. But rigid and useful theoretical study is rare up to date. A systematic analysis and a detailed survey of this problem were presented in this paper. From a system's perspective, it shows that combining MHs with problem instances' key properties, algorithm characters and human intelligence is a right way to deal with this difficulty.

An ant colony optimization heuristic for solving maximum independent set problems

Abstract: In this paper, ant colony optimization heuristic is extended for solving maximum independent set (MIS) problems. MIS problems are quite different from the travelling salesman problems (TSP) etc., in which no concept of "path or order" exists in its solutions. Based on such characteristics, the ant colony optimization heuristic is modified in this paper in the following ways: (i) a new computation method for heuristic information is adapted; (ii) the pheromone update rule is augmented; (iii) a complement solution construction process is designed. The simulation shows that the proposed ant colony optimization heuristic is effective and efficient for MIS problems.

Population extremal optimization-based extended distributed model predictive load frequency control of multi-area interconnected power systems

Abstract: How to design a set of optimal distributed load frequency controllers for a multi-area interconnected power system is an important but still challenging issue in the field of modern electric power systems. This paper presents an adaptive population extremal optimization-based extended distributed model predictive load frequency control method called PEO-EDMPC for a multi-area interconnected power system. The key idea behind the proposed method is formulating the dynamic load frequency control issue of each area power system as an extended distributed discrete-time state-space model based on an extended state vector, obtaining a distributed dynamic extended predictive model, and rolling optimization of real-time control output signal by adopting an adaptive population extremal optimization algorithm, where the fitness is evaluated by the weighted sum of square predicted errors and square future control values. The superiority of the proposed PEO-EDMPC method to a traditional distributed model predictive control method, a population extremal optimization-based distributed proportional-integral control algorithm and a traditional distributed integral control method is demonstrated by the simulation studies on two-area and three-area interconnected power systems in cases of normal, perturbed system parameters and dynamical load disturbances.