Metaheuristic Algorithms

In this research, a new metaheuristic optimization algorithm, inspired by biological nervous systems and artificial neural networks (ANNs) is proposed for solving complex optimization problems. The proposed method, named as neural network algorithm (NNA), is developed based on the unique structure of ANNs. The NNA benefits from complicated structure of the ANNs and its operators in order to generate new candidate solutions. In terms of convergence proof, the relationship between improvised exploitation and each parameter under asymmetric interval is derived and an iterative convergence of NNA is proved theoretically. In this paper, the NNA with its interconnected computing unit is examined for 21 well-known unconstrained benchmarks with dimensions 50–200 for evaluating its performance compared with the state-of-the-art algorithms and recent optimization methods. Besides, several constrained engineering design problems have been investigated to validate the efficiency of NNA for searching in feasible region in constrained optimization problems. Being an algorithm without any effort for fine tuning initial parameters and statistically superior can distinguish the NNA over other reported optimizers. It can be concluded that, the ANNs and its particular structure can be successfully utilized and modeled as metaheuristic optimization method for handling optimization problems.

Effective scheduling systems are essential for client efficiency and satisfaction, especially for dynamic financial services industry. For an example there are many clients who come directly to request the transaction data and mediation process without an appointment in advance. This will cause the scheduling process cannot be done on time following client's demand as constrained by schedule activities of directors who already have other fixed agenda that has been planned previously. We analyze some scheduling algorithm to optimally balance the scheduling of clients by considering the broker, clients and director fixed agenda to solve scheduling problem in Legal and Membership Division of PT. Jakarta Futures Exchange (JFX). In this paper, we analyze three different algorithm programming methods to solve the diversity of client's demands. Experiment result show shortest job first (SJF) algorithm has lowest average waiting time for mediation scheduling comparing with FCFS and Round Robin algorithm.

Use of pipelined ADCs is becoming increasingly popular both as stand alone parts and as embedded functional units in SOC design. They have acceptable resolution and high speed of operation and can be placed in relatively small area. The design is implemented in 0.18uM CMOS process. The design includes a folded cascode op-amp with a unity gain frequency of 200MHz at 88 deg. Phase margin and a dc gain of 75dB. The circuit employs a built in sample and hold circuit and a three phase non-overlapping clock.

The Bees Algorithm (BA) is one of the most recent swarm-based metaheuristic algorithms that mimic the natural foraging behavior of honey bees in order to solve optimization problems and find the optimal solution. Clustering analysis, used in various science fields and applications, is an important tool and a descriptive process attempting to identify similar classes of objects based on the values of their attributes. To solve clustering problems there are diverse ways, including machine learning techniques, statistics, and metaheuristic methods. In this work, an improved Bees Algorithm with memory scheme (BAMS), which is a modified version of the BA algorithm, is used for data clustering. In the BAMS algorithm, a simple memory scheme is introduced to prevent visiting sites which are close to previously visited sites and to avoid visiting sites with the same fitness or worse. Four real-life data sets are applied to validate the proposed algorithm, and results of this study are compared to BA and others state-of-the-art methods. The experimental results show that the proposed algorithm outperforms other methods.

The International Journal of Wireless & Mobile Networks (IJWMN) is a bi monthly open access peer-reviewed journal that publishes articles which contribute new results in all areas of Wireless & Mobile Networks. The journal focuses on all technical and practical aspects of Wireless & Mobile Networks. The goal of this journal is to bring together researchers and practitioners from academia and industry to focus on advanced wireless & mobile networking concepts and establishing new collaborations in these areas. Authors are solicited to contribute to this journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in Wireless and Mobile Networks. Topics of interest include, but are not limited to, the following • Architectures, protocols, and algorithms to cope with mobile & wireless Networks • Distributed algorithms of mobile computing • OS and middleware support for mobile computing and networking • Routing, and communication primitives in ad-hoc and sensor networks • Synchronization and scheduling issues in mobile and ad hoc networks • Resource management in mobile, wireless and ad-hoc networks • Data management on mobile and wireless computing • Integration of wired and wireless networks • Broadband access networks • Energy saving protocols for ad hoc and sensor networks • Complexity analysis of algorithms for mobile environments • Information access in wireless networks • Algorithms and modeling for tracking and locating mobile users • Satellite communications • Cryptography, security and privacy of mobile & wireless networks • Performance of mobile and wireless networks and systems • Mobile ad hoc and sensor networks • Wireless multimedia systems • Service creation and management environments for mobile/wireless systems