A Wireless Sensor Network (WSN) is a group of autonomous sensors that are distributed geographically. However, sensor nodes in WSNs battery-powered, and the energy drainage significant issue. The clustering approach holds an imperative part boosting lifespan WSNs. This gathers into clusters selects cluster heads (CHs). CHs accumulate information from members transfer data to base station (BS). ...

Krill herd (KH) has been proven to be an efficient algorithm for function optimization. For some complex functions, this algorithmmay have problems with convergence or being trapped in local minima. To cope with these issues, this paper presents an improved KH-based algorithm, called Opposition Krill Herd (OKH). The proposed approach utilizes opposition-based learning (OBL), position clamping (...

background and purpose: studies have shown the advantages of e-cme programs. developing case-based e-cme activities, one of the popular formats of e-cme programs, is difficult and time consuming. in this article we describe our experience of performing instructional system design for creating case-based e-cme contents.  methods: we performed a five-step instructional system design (i.e. system ...

CODEQ is a new, population-based meta-heuristic algorithm that is a hybrid of concepts from chaotic search, opposition-based learning, differential evolution and quantum mechanics. CODEQ has successfully been used to solve different types of problems (e.g. constrained, integer-programming, engineering) with excellent results. In this paper, CODEQ is used to train feed-forward neural networks. T...

In this article, an Advanced Charged System Search (ACSS) algorithm is applied for the optimum design of steel structures. ACSS uses the idea of Opposition-based Learning and Levy flight to enhance the optimization abilities of the standard CSS. It also utilizes the information of the position of each charged particle in the subsequent search process to increase the convergence speed. The objec...

The slow convergence and local minima problems associated with neural networks (NN) used for non-linear system identification have been resolved by evolutionary techniques such as differential evolution (DE) combined with Levenberg Marquardt (LM) algorithm. In this work the authors attempted further to employ an opposition based learning in DE, known as opposition based differential evolution (...

In this paper we present a new Discrete Particle Swarm Optimization approach to induce rules from discrete data. The proposed algorithm, called Opposition‐ based Natural Discrete PSO (ONDPSO), initializes its population by taking into account the discrete nature of the data. Particles are encoded using a Natural Encoding scheme. Each member of the population updates i...