Opportunistic Self Organizing Migrating Algorithm for Real-Time Dynamic Traveling Salesman Problem

Self Organizing Migrating Algorithm (SOMA) is a meta-heuristic algorithm based on the self-organizing behavior of individuals in a simulated social environment. SOMA performs iterative computations on a population of potential solutions in the given search space to obtain an optimal solution. In this paper, an Opportunistic Self Organizing Migrating Algorithm (OSOMA) has been proposed that introduces a novel strategy to generate perturbations effectively. This strategy allows the individual to span across more possible solutions and thus, is able to produce better solutions. A comprehensive analysis of OSOMA on multi-dimensional unconstrained benchmark test functions is performed. OSOMA is then applied to solve real-time Dynamic Traveling Salesman Problem (DTSP). The problem of real-time DTSP has been stipulated and simulated using real-time data from Google Maps with a varying cost-metric between any two cities. Although DTSP is a very common and intuitive model in the real world, its presence in literature is still very limited. OSOMA performs exceptionally well on the problems mentioned above. To substantiate this claim, the performance of OSOMA is compared with SOMA, Differential Evolution and Particle Swarm Optimization.Comment: 6 pages, published in CISS 201

Novel Selective Mapping with Oppositional Hosted Cuckoo Optimization Algorithm for PAPR Reduction in 5G UFMC Systems

In recent times, there is a continuous requirement of achieving high data rates owing to an increase in the number of devices and significant demand for various services with maximum reliability and minimum delay. It results in the development of fifth generation (5G) to offer better services with enhanced data rate. Recently, a major alternative to OFDM technology for 5G networks called universal filtered multi-carrier (UFMC) is presented where every individual sub-band is filtered that reduces the OOB radiation and eliminates guard band. But high peak-to-average power ratio (PAPR) is a crucial issue which arises from the utilization of several subcarriers to generate the time domain transmission signal. For resolving this issue, this paper presents a novel selective mapping with oppositional hosted cuckoo optimization (SM-OHOCO) algorithm for PAPR reduction in 5G UFMC systems. In the SM-OHOCO algorithm, rather than the generation of several random phase sequences, SM-OHOCO algorithm is performed iteratively to attain a better solution with few searching rounds, showing the novelty of the work. As the optimization of phase sequence in the SLM technique is considered as an NP hard optimization problem, the OHOCO algorithm is applied, which is derived by incorporating the concepts of the HOCO algorithm with oppositional based learning (OBL) strategy. To validate the effective performance of the proposed SM-OHOCO algorithm, an extensive experimental analysis is performed to highlight the improved performance in 5G networks. The resultant values pointed out the superior outcome of the proposed SM-OHOCO algorithm over the other existing methods in terms of distinct measure

On the performance of the hybridisation between migrating birds optimisation variants and differential evolution for large scale continuous problems

Migrating Birds Optimisation (mbo) is a nature-inspired approach which has been shown to be very effective when solving a variety of combinatorial optimisation problems. More recently, an adaptation of the algorithm has been proposed that enables it to deal with continuous search spaces. We extend this work in two ways.Firstly, a novel leader replacement strategy is proposed to counter the slow convergence of the existing mbo algorithms due to low selection pressure. Secondly, mbo is hybridised with adaptive neighbourhood operators borrowed from Differential Evolution (de) that promote exploration and exploitation. The new variants are tested on two sets of continuous large scale optimisation problems. Results show that mbo variants using adaptive, exploration-based operators outperform de on the cec benchmark suite with 1000variables. Further experiments on a second suite of 19 problems show that mbo variants outperform de on 90% of these test-cases

A Multi-objective Optimization Model for Virtual Machine Mapping in Cloud Data Centres

© 2016 IEEE. Modern cloud computing environments exploit virtualization for efficient resource management to reduce computational cost and energy budget. Virtual machine (VM) migration is a technique that enables flexible resource allocation and increases the computation power and communication capability within cloud data centers. VM migration helps cloud providers to successfully achieve various resource management objectives such as load balancing, power management, fault tolerance, and system maintenance. However, the VM migration process can affect the performance of applications unless it is supported by smart optimization methods. This paper presents a multi-objective optimization model to address this issue. The objectives are to minimize power consumption, maximize resource utilization (or minimize idle resources), and minimize VM transfer time. Fuzzy particle swarm optimization (PSO), which improves the efficiency of conventional PSO by using fuzzy logic systems, is relied upon to solve the optimization problem. The model is implemented in a cloud simulator to investigate its performance, and the results verify the performance improvement of the proposed model

Metaheuristic Algorithm for Photovoltaic Parameters: Comparative Study and Prediction with a Firefly Algorithm

In this paper, a Firefly algorithm is proposed for identification and comparative study of five, seven and eight parameters of a single and double diode solar cell and photovoltaic module under different solar irradiation and temperature. Further, a metaheuristic algorithm is proposed in order to predict the electrical parameters of three different solar cell technologies. The first is a commercial RTC mono-crystalline silicon solar cell with single and double diodes at 33 °C and 1000 W/m2. The second, is a flexible hydrogenated amorphous silicon a-Si:H solar cell single diode. The third is a commercial photovoltaic module (Photowatt-PWP 201) in which 36 polycrystalline silicon cells are connected in series, single diode, at 25 °C and 1000 W/m2 from experimental current-voltage. The proposed constrained objective function is adapted to minimize the absolute errors between experimental and predicted values of voltage and current in two zones. Finally, for performance validation, the parameters obtained through the Firefly algorithm are compared with recent research papers reporting metaheuristic optimization algorithms and analytical methods. The presented results confirm the validity and reliability of the Firefly algorithm in extracting the optimal parameters of the photovoltaic solar cell

Projectile-target search algorithm: a stochastic metaheuristic optimization technique

This paper proposes a new stochastic metaheuristic optimization algorithm which is based on kinematics of projectile motion and called projectile-target search (PTS) algorithm. The PTS algorithm employs the envelope of projectile trajectory to find the target in the search space. It has 2 types of control parameters. The first type is set to give the possibility of the algorithm to accelerate convergence process, while the other type is set to enhance the possibility to generate new better projectiles for searching process. However, both are responsible to find better fitness values in the search space. In order to perform its capability to deal with global optimum problems, the PTS algorithm is evaluated on six well-known benchmarks and their shifted functions with 100 dimensions. Optimization results have demonstrated that the PTS algoritm offers very good performances and it is very competitive compared to other metaheuristic algorithm