Economic dispatch with valve point effect and Prohibited Operating Zones (POZs) is a non-convex and discontinuous optimization problem. Harmony Search (HS) is one of the recently presented meta-heuristic algorithms for solving optimization problems, which has different variants. The performances of these variants are severely affected by selection of different parameters of the algorithm. Intel...

This paper proposes a hybrid harmony search algorithm that incorporates method of reinitializing harmonies memory using particle swarm optimization with an improved opposition-based learning (IOBL) to solve continuous problems. allows the obtain better results by increasing space solutions. approach has been validated comparing performance proposed state-of-the-art algorithm, solving fifteen st...

In this paper, we put forward a novel approach based on hierarchical teaching-and-learning-based optimization (HTLBO) algorithm for nonlinear camera calibration. This algorithm simulates the teaching-learning ability of teachers and learners of a classroom. Different from traditional calibration approach, the proposed technique can find the nearoptimal solution without the need of accurate init...

Most of the well-established particle swarm optimization (PSO) variants do not provide alternative learning strategies when particles fail to improve their fitness during the searching process. To solve this issue, we improved the state-of-art teaching–learningbased optimization algorithm and adapted the enhanced framework into the PSO. Thus, we developed a bidirectional teaching and peer-learn...

There have been numerous biologically inspired algorithms used to train feed-forward artificial neural networks such as generic algorithms, particle swarm optimization and ant colony optimization. The Harmony Search (HS) algorithm is a stochastic meta-heuristic that is inspired from the improvisation process of musicians. HS is used as an optimization method and reported to be a competitive alt...

In this paper, an improved global-best harmony search algorithm, named IGHS, is proposed. In the IGHS algorithm, initialization based on opposition-based learning for improving the solution quality of the initial harmony memory, a new improvisation scheme based on differential evolution for enhancing the local search ability, a modified random consideration based on artificial bee colony algori...

Bayesian networks are a widely used graphical model with diverse applications in knowledge discovery, classification, and decision making. Learning a Bayesian network from discrete data can be cast as a combinatorial optimization problem and thus solved using optimization techniques—the well-known score-and-search approach. An important consideration when applying a score-andsearch method for B...

Metaheuristic algorithms currently represent the standard approach to engineering optimization. A very challenging field is large-scale structural optimization, entailing hundreds of design variables and thousands nonlinear constraints on element stresses nodal displacements. However, few studies documented use metaheuristic in In order fill this gap, an enhanced hybrid harmony search (HS) algo...

In this paper we consider a particular class of nonlinear optimization problems involving both continuous and discrete variables. The distinguishing feature of this class of nonlinear mixed optimization problems is that the structure and the number of variables of the problem depend on the values of some discrete variables. In particular we define a general algorithm model for the solution of t...