Seeker optimization algorithm (SOA) is a novel search algorithm based on simulating the act of human searching, which has been shown to be a promising candidate among search algorithms for unconstrained function optimization. In this article we propose a modified seeker optimization algorithm. In order to enhance the performance of SOA, our proposed approach uses two search equations for produc...

In this paper, a gradient-based back propagation dynamical iterative learning algorithm is proposed for structure optimization and parameter tuning of the neuro-fuzzy system. Premise and consequent parameters of the neuro-fuzzy model are initialized randomly and then tuned by the proposed iterative algorithm. The learning algorithm is based on the first order partial derivative of the output wi...

The paper discusses three major issues. First, it discusses why it makes sense to approach problems in a hierarchical fashion. It deenes the class of hierarchically decomposable functions that can be used to test the algorithms that approach problems in this fashion. Finally, the Bayesian optimization algorithm (BOA) is extended in order to solve the proposed class of problems.

This paper presents an optimization example using a new paradigm for viewing the work of Wireless Sensor Networks. In our earlier paper [1] the Observed Field (OF) is described as a multi-dimensional “Information Space” (ISp). The Wireless Sensor Network is described as a “Transformation Space” (TS), while the information collector is a single point consumer of information, described as an “Inf...

The fields of machine learning and mathematical optimization increasingly intertwined. The special topic on supervised learning and convex optimization examines this interplay. The training part of most supervised learning algorithms can usually be reduced to an optimization problem that minimizes a loss between model predictions and training data. While most optimization techniques focus on ac...

in this paper, the gain in ld-celp speech coding algorithm is predicted using three neural models, that are equipped by genetic and particle swarm optimization (pso) algorithms to optimize the structure and parameters of neural networks. elman, multi-layer perceptron (mlp) and fuzzy artmap are the candidate neural models. the optimized number of nodes in the first and second hidden layers of el...

This study is devoted to discrete sizing optimization of truss structures employing an efficient discrete evolutionary meta-heuristic algorithm which uses the Newton gradient-based method as its updating scheme and it is named here as Newton Meta-heuristic Algorithm (NMA). In order to enable the NMA population-based meta-heuristic to effectively explore the discrete design space, a term contain...

Parallelizable optimization techniques are applied to the problem of learning in feedforward neural networks. In addition to having superior convergence properties, optimization techniques such as the PolakRibiere method are also significantly more efficient than the Backpropagation algorithm. These results are based on experiments performed on small boolean learning problems and the noisy real...