Topic
Extremal optimization
About: Extremal optimization is a research topic. Over the lifetime, 1168 publications have been published within this topic receiving 104943 citations.
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TL;DR: Simulations show that the hybrid algorithm has remarkable global convergence ability, and can avoid the premature convergence effectively.
Abstract: A new hybrid algorithm based on Differential Evolution (DE) and Extremal Optimization (EO) is proposed to solve the premature convergence and low precision of standard differential evolution when applied to complex optimization problems. The key points of it lie in: the hybrid algorithm introduced the population-based Extremal Optimization algorithm in the iteration process of DE when population aggregation got the high degree, which uses the volatility of EO to increase the diversity of population and the ability of breaking away from the local optimum. Simulations show that the hybrid algorithm has remarkable global convergence ability, and can avoid the premature convergence effectively.
2 citations
28 Nov 2015
TL;DR: A new improved ant colony optimization algorithm was realized to deal with structures optimization problems efficiently, with several special improvements, written by the MATLAB language.
Abstract: This paper highlighted a new improved ant colony optimization algorithm was realized to deal with structures optimization problems efficiently, with several special improvements. The new algorithm procedure was written by the MATLAB language. The numerical simulation results demonstrated that this new methodology revealed more obvious advantage at truss structural optimization problems in discrete system. For the civil engineering designer, this new method is very simple and practical, and much more suitable.
2 citations
TL;DR: The methodology applied to model the LEDs, together with the theoretical basis for CCT and CRI calculation, is presented and a comparative result analysis of M-GEO evolutionary algorithm with the Levenberg–Marquardt conventional deterministic algorithm is presented.
Abstract: Daylight illuminants are widely used as references for color quality testing and optical vision testing applications. Presently used daylight simulators make use of fluorescent bulbs that are not tunable and occupy more space inside the quality testing chambers. By designing a spectrally tunable LED light source with an optimal number of LEDs, cost, space, and energy can be saved. This paper describes an application of the generalized extremal optimization (GEO) algorithm for selection of the appropriate quantity and quality of LEDs that compose the light source. The multiobjective approach of this algorithm tries to get the best spectral simulation with minimum
fitness error toward the target spectrum, correlated color temperature (CCT) the same as the target spectrum, high color rendering index (CRI), and luminous flux as required for testing applications. GEO is a global search algorithm based on phenomena of natural evolution and
is especially designed to be used in complex optimization problems. Several simulations have been conducted to validate the performance of the algorithm. The methodology applied to model the LEDs, together with the theoretical basis for CCT and CRI calculation, is presented in this
paper. A comparative result analysis of M-GEO evolutionary algorithm with the Levenberg– Marquardt conventional deterministic algorithm is also presented
2 citations
TL;DR: Cooperative metaheuristics underlain by ant colony optimization and MH-method algorithms are developed, and the efficiency of the proposed methodology is evaluated by means of a computational experiment.
Abstract: The paper proposes a methodology to construct cooperative metaheuristic methods for solving combinatorial optimization problems using model-based algorithms. Its distinctive feature is that the original problem is solved by a search (optimization) in the space of models. Such a search is performed on the basis of models formed by basic algorithms. Cooperative metaheuristics underlain by ant colony optimization and MH-method algorithms are developed, and the efficiency of the proposed methodology is evaluated by means of a computational experiment.
2 citations
10 Jun 2011
TL;DR: The proposed algorithm is applied to four MOPs in engineering design by comparison with other multi-objective evolutionary algorithms (MOEAs) and the results indicate the algorithm is able to find better and much wider spread of solutions.
Abstract: A new hybrid multi-objective optimization (MO) solution with the combination of Particle Swarm Optimization (PSO) and Extremal Optimization (EO), called “PSO-EO-MO”, was presented in authors' early studies. The proposed algorithm is based on the superior functionalities of PSO for searching a Pareto dominance and extremal dynamics oriented EO for fine tuning and adjustment. The concept of crowding and lattice for the external archive is also employed for diversity preservation and getting a well-distributed sets of non-dominated solutions. Based on our previous studies, in this study the proposed algorithm is applied to four MOPs in engineering design by comparison with other multi-objective evolutionary algorithms (MOEAs). The results indicate the algorithm is able to find better and much wider spread of solutions. Consequently, the proposed solution may be applied to more complex real-world MOPs.
2 citations