Extremal optimization

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

More Natural Optimization Algorithms

Abstract: 187 The GA is not the only optimization algorithm that models natural processes. In this chapter we briefly present some of the current algorithms being used for global optimization. Some introductory programs are included for your amusement. Which algorithm is best? We tend to like the GA and some of the local optimization algorithms. The “No Free Lunch Theorem” says that the averaged performance of all search algorithms over all problems is equal (Wolpert, 1997). In other words, the GA performs no better than a totally random search when applied to all problems. Thus the idea is to use the right algorithm for the right problem.

A kind of iterative improvement based ant colony optimization algorithm for the traveling salesman problem

Abstract: Classical ant colony optimization algorithms build solutions by starting with an empty initial solution,and unconditionally accepting selected components.This has become a natural restriction of its intensification ability.To overcome this shortage,an iterative improvement based ant colony optimization algorithm was presented for the traveling salesman problem.In the process of constructing the solution,the ant always memorizes a complete solution;and it adopts a candidate city only when such an adoption can improve the solution.Reconstructing of a partial solution was used to keep the diversity of swarm and avoid premature convergence.Simulation results showed that the proposed algorithm can obtain better solutions within less iteration numbers.

Game theory, Extremal optimization, and Community Structure Detection in Complex Networks

Abstract: The network community detection problem consists in identifying groups of nodes that are more densely connected to each other than to the rest of the network. The lack of a formal definition for the notion of community led to the design of various solution concepts and computational approaches to this problem, among which those based on optimization and, more recently, on game theory, received a special attention from the heuristic community. The former ones define the community structure as an optimum value of a fitness function, while the latter as a game equilibrium. Both are appealing as they allowed the design and use of various heuristics. This paper analyses the behavior of such a heuristic that is based on extremal optimization, when used either as an optimizer or within a game theoretic setting. Numerical results, while significantly better than those provided by other state-of-art methods, for some networks show that differences between tested scenarios do not indicate any superior behavior when using game theoretic concepts; moreover, those obtained without using any selection for survival suggest that the search is actually guided by the inner mechanism of the extremal optimization method and by the fitness function used to evaluate and compare components within an individual.

Hybrid Extremal Optimization and Glowworm Swarm Optimization

Abstract: Glowworm Swarm Optimization algorithm is applied for the simultaneous capture of multiple optima of multimodal functions. In this paper, we have attempted to create a Hybrid Extremal Glowworm Swarm Optimization (HEGSO) algorithm. Aiming at the glowworm swarm optimization algorithm is easy to fall into local optima, having low accuracy, and to be unable to find the best local optima. However for solving these problems, the present algorithm has been increased the probability of choosing the best local optima. Moreover we want to use this method to have a best movement for agents in Glow worm optimization algorithm. Simulation and comparison based on several well-studied benchmarks demonstrate the effectiveness, efficiency and robustness of the proposed algorithms.

An Integer-Coded Chaotic Particle Swarm Optimization for Traveling Salesman Problem

Abstract: Traveling Salesman Problem (TSP) is one of NP-hard combinatorial optimization problems, which will experience “combination explosion” when the problem goes beyond a certain size. Therefore, it has been a hot topic to search an effective solving method. The general mathematical model of TSP is discussed, and its permutation and combination based model is presented. Based on these, Integer-coded Chaotic Particle Swarm Optimization for solving TSP is proposed. Where, particle is encoded with integer; chaotic sequence is used to guide global search; and particle varies its positions via “flying”. With a typical 20-citys TSP as instance, the simulation experiment of comparing ICPSO with GA is carried out. Experimental results demonstrate that ICPSO is simple but effective, and better than GA at performance.