Recent theoretical research has shown that self-adjusting and self-adaptive mechanisms can provably outperform static settings in evolutionary algorithms for binary search spaces. However, the vast majority of these studies focuses on unimodal functions which do not require algorithm to flip several bits simultaneously make progress. In fact, existing are designed detect local optima have any o...

Evolutionary computation is inspired by nature in order to formulate metaheuristics capable to optimize several kinds of problems. A family of algorithms has emerged based on this idea; e.g. genetic algorithms, evolutionary strategies, particle swarm optimization (PSO), ant colony optimization (ACO), etc. In this paper we show a populationbased metaheuristic inspired on the gravitational forces...

The calibration of complex models of biological systems requires numerical simulation and optimization procedures to infer undetermined parameters and fit measured data. The optimization step typically employs heuristic global optimization algorithms, but due to measurement noise and the many degrees of freedom, it is not guaranteed that the identified single optimum is also the most meaningful...

Many real practical applications are often needed to find more than one optimum solution. Existing Evolutionary Algorithm (EAs) are originally designed to search the unique global value of the objective function. The present work proposed an improved niching based scheme named spatially neighbors best search technique combine with crowding-based differential evolution (SnbDE) for multimodal opt...

Particle swarm optimization (PSO) has received increasing interest from the optimization community due to its simplicity in implementation and its inexpensive computational overhead. However, PSO has premature convergence, especially in complex multimodal functions. Extremal Optimization (EO) is a recently developed local-search heuristic method and has been successfully applied to a wide varie...

The calibration of complex models of biological systems requires numerical simulation and optimization procedures to infer undetermined parameters and fit measured data. The optimization step typically employs heuristic global optimization algorithms, but due to measurement noise and the many degrees of freedom, it is not guaranteed that the identified single optimum is also the most meaningful...

due to the fact that the error surface of adaptive infinite impulse response (iir) systems is generally nonlinear and multimodal, the conventional derivative based techniques fail when used in adaptive identification of such systems. in this case, global optimization techniques are required in order to avoid the local minima. harmony search (hs), a musical inspired metaheuristic, is a recently ...

The paper describes software combining parametric geometric modeling with a version of the harmony search method, modified to support multimodal structural optimization. Researchers have recognized the potential of population-based optimization methods, such as genetic algorithms, to support multimodal optimization: that is, generating a diverse range of good alternative solutions, rather than ...