Extremal optimization

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

An Approximate Algorithm Combining P Systems and Ant Colony Optimization for Taraveling Salesman Problems

Abstract: This paper proposes an approximate optimization algorithm combining P systems with ant colony optimization, called ACOPS, to solve traveling salesman problems, which are well-known and extensively studied NP-complete combinatorial optimization problems. ACOPS uses the pheromone model and pheromone update rules defined by ant colony optimization algorithms, and the hierarchical membrane structure and transformation/communication rules of P systems. First, the parameter setting of the ACOPS is discussed. Second, extensive experiments and statistical analysis are investigated. It is shown that the ACOPS is superior to Nishida’s algorithms and its counterpart ant colony optimization algorithms, in terms of the quality of solutions and the number of function evaluations.

A Novel Hybrid Intelligence Algorithm for Solving Combinatorial Optimization Problems

Abstract: The ant colony optimization (ACO) algorithm is a new heuristic algorithm that offers good robustness and searching ability. With in-depth exploration, the ACO algorithm exhibits slow convergence speed, and yields local optimization solutions. Based on analysis of the ACO algorithm and the genetic algorithm, we propose a novel hybrid genetic ant colony optimization (NHGAO) algorithm that integrates multi-population strategy, collaborative strategy, genetic strategy, and ant colony strategy, to avoid the premature phenomenon, dynamically balance the global search ability and local search ability, and accelerate the convergence speed. We select the traveling salesman problem to demonstrate the validity and feasibility of the NHGAO algorithm for solving complex optimization problems. The simulation experiment results show that the proposed NHGAO algorithm can obtain the global optimal solution, achieve self-adaptive control parameters, and avoid the phenomena of stagnation and prematurity.

Design-encoding for evolutionary algorithms in the field of structural optimization

Abstract: With the rapid development of computing technology during recent years the opportunities of simulating physical processes has increased. Particularly the use of Finite Element Methods (FEM) in engineering allows ever more accuracy in predicting the response of systems. This has massively reduced the necessity of extensive practice tests. The ability to simulate system responses soon inspires the desire to optimize these systems within automated loops. However, this poses further requirements on the simulation. .The optimization routine must be able to change the system to be optimized, or more precisely the information representing this system, in an effective way since otherwise no non-trivial optimization is possible. For this, some kind of interface or information encoding scheme midway between the mere simulation and the automated optimization is necessary. In the field of Structural Optimization the encoded information typically represents some structural design. Thus, we refer to this information encoding in a more specific way as design-encoding. This work deals with the development and application of adequate concepts and methods for efficient design-encoding for Structural Optimization. Most practical applications in this field are shown to be quite heavily heterogeneous in the sense that it is often difficult, if not impossible, to formulate objective and constraining functions so that they are accessible to classic mathematical programming methods. Consequently, the optimization paradigms used throughout this thesis are based on Evolutionary Algorithms. This approach lets us handle virtually any constraint of the optimization problem. Furthermore, we are not limited to some specific kind of number or parameter interface which are typically necessary for mathematical programming and are often used in context with other methods as well, For this reason we are free to consider the topic of design-encoding in a very general manner. Such an optimization interface is essentially some kind of designencoding scheme, equipped with the functionalities allowing an optimization routine to manipulate the corresponding design. Ideally, the encoding allows the mapping of all conceivable solutions to a problem without the undesired effect of possible a-priori exclusion of potentially relevant solutions.

Quantum bacterial foraging optimization algorithm

Abstract: This paper proposes a novel swarm intelligence optimization method which integrates bacterial foraging optimization (BFO) with quantum computing, called quantum bacterial foraging optimization (QBFO) algorithm. In QBFO, a multi-qubit which can represent a linear superposition of states in search space probabilistically is used to represent a bacterium, so that the quantum bacteria representation has a better characteristic of population diversity. A quantum rotation gate is designed to simulate the chemotactic step to drive the bacteria toward better solutions. Several tests are conducted based on benchmark functions including multi-peak function to evaluate optimization performance of the proposed algorithm. The numeric results show that the proposed QBFO has more powerful properties in convergence rate, stability and the ability of searching for the global optimal solution than the original BFO and quantum genetic algorithm. In addition, we applied our proposed QBFO to solve the traveling salesman problem, which is a well-known NP-hard problem in combinatorial optimization. The results indicate that the proposed QBFO shows better convergence behavior without premature convergence, and has more powerful properties in convergence rate, stability and the ability of searching for the global optimal solution, as compared to ant colony optimization algorithm and quantum genetic algorithm.

Generalized Extremal Optimization: a competitive algorithm for test data generation

Abstract: Software testing is an important part of the software development pro- cess, and automating test data generation contributes to reducing cost and time efforts. It has recently been shown that evolutionary algorithms (EAs), such as the Genetic Algorithms (GAs), are valuable tools for test data generation. This work assesses the performance of a recently proposed EA, the Generalized Extremal Optimization (GEO), on test data generation for programs that have paths with loops. Benchmark programs were used as study cases and GEO's performance was compared to the one of a GA. Results showed that using GEO required much less computational effort than GA on test data generation and also on internal parameter setting. These results indicate that GEO is an at- tractive option to be used for test data generation.