scholarly journals An Intelligent Optimized Route-Discovery Model for IoT-Based VANETs

Processes ◽  
2021 ◽  
Vol 9 (12) ◽  
pp. 2171
Author(s):  
Dinesh Karunanidy ◽  
Rajakumar Ramalingam ◽  
Ankur Dumka ◽  
Rajesh Singh ◽  
Ibrahim Alsukayti ◽  
...  
Keyword(s):  
Vehicular Ad Hoc Networks ◽  
Intelligent Transportation System ◽  
Optimization Problems ◽  
Selection Process ◽  
Optimal Path ◽  
Cuckoo Search ◽  
Primary Objective ◽  
Grey Wolf Optimizer ◽  
Route Discovery ◽  
Optimal Arrangement

Intelligent Transportation system are becoming an interesting research area, after Internet of Things (IoT)-based sensors have been effectively incorporated in vehicular ad hoc networks (VANETs). The optimal route discovery in a VANET plays a vital role in establishing reliable communication in uplink and downlink direction. Thus, efficient optimal path discovery without a loop-free route makes network communication more efficient. Therefore, this challenge is addressed by nature-inspired optimization algorithms because of their simplicity and flexibility for solving different kinds of optimization problems. NIOAs are copied from natural phenomena and fall under the category of metaheuristic search algorithms. Optimization problems in route discovery are intriguing because the primary objective is to find an optimal arrangement, ordering, or selection process. Therefore, many researchers have proposed different kinds of optimization algorithm to maintain the balance between intensification and diversification. To tackle this problem, we proposed a novel Java macaque algorithm based on the genetic and social behavior of Java macaque monkeys. The behavior model mimicked from the Java macaque monkey maintains well-balanced exploration and exploitation in the search process. The experimentation outcome depicts the efficiency of the proposed Java macaque algorithm compared to existing algorithms such as discrete cuckoo search optimization (DCSO) algorithm, grey wolf optimizer (GWO), particle swarm optimization (PSO), and genetic algorithm (GA).

scholarly journals New Discrete Cuckoo Search Optimization Algorithms for Effective Route Discovery in IoT-Based Vehicular Ad-Hoc Networks

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 145469-145488
Author(s):  
Habeeb Bello-Salau ◽  
Adeiza James Onumanyi ◽  
Adnan M. Abu-Mahfouz ◽  
Achonu O. Adejo ◽  
Muhammed Bashir Mu'Azu
Keyword(s):  
Ad Hoc Networks ◽  
Vehicular Ad Hoc Networks ◽  
Ad Hoc ◽  
Optimization Algorithms ◽  
Cuckoo Search ◽  
Route Discovery ◽  
Search Optimization ◽  
Cuckoo Search Optimization ◽  
Hoc Networks

scholarly journals Optimization of Complex System Reliability using Hybrid Grey Wolf Optimizer

2021 ◽  
Vol 4 (2) ◽  
pp. 241-256
Author(s):  
Ganga Negi ◽  
◽  
Anuj Kumar ◽  
Sangeeta Pant ◽  
Mangey Ram ◽  
...  
Keyword(s):  
Optimization Problems ◽  
Optimization Technique ◽  
Cuckoo Search ◽  
Optimization Methods ◽  
Grey Wolf Optimizer ◽  
Grey Wolf ◽  
Reliability Allocation ◽  
Search Optimization ◽  
Cuckoo Search Optimization ◽  
Comparison Of The Results

Reliability allocation to increase the total reliability has become a successful way to increase the efficiency of the complex industrial system designs. A lot of research in the past have tackled this problem to a great extent. This is evident from the different techniques developed so far to achieve the target. Stochastic metaheuristics like simulated annealing, Tabu search (TS), Particle Swarm Optimization (PSO), Cuckoo Search Optimization (CS), Genetic Algorithm (GA), Grey wolf optimization technique (GWO) etc. have been used in recent years. This paper proposes a framework for implementing a hybrid PSO-GWO algorithm for solving some reliability allocation and optimization problems. A comparison of the results obtained is done with the results of other well-known methods like PSO, GWO, etc. The supremacy/competitiveness of the proposed framework is demonstrated from the numerical experiments. These results with regard to the time taken for the computation and quality of solution outperform the previously obtained results by the other well-known optimization methods.

Modified Cuckoo Search Algorithm using a New Selection Scheme for Unconstrained Optimization Problems

2020 ◽  
Vol 16 (4) ◽  
pp. 307-315 ◽  
Author(s):  
Mohammad Shehab ◽  
Ahamad Tajudin Khader
Keyword(s):  
Unconstrained Optimization ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Selection Process ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Random Selection ◽  
Selection Scheme ◽  
Unconstrained Optimization Problems ◽  
Tournament Selection

Background: Cuckoo Search Algorithm (CSA) was introduced by Yang and Deb in 2009. It considers as one of the most successful in various fields compared with the metaheuristic algorithms. However, random selection is used in the original CSA which means there is no high chance for the best solution to select, also, losing the diversity. Methods: In this paper, the Modified Cuckoo Search Algorithm (MCSA) is proposed to enhance the performance of CSA for unconstrained optimization problems. MCSA is focused on the default selection scheme of CSA (i.e. random selection) which is replaced with tournament selection. So, MCSA will increase the probability of better results and avoid the premature convergence. A set of benchmark functions is used to evaluate the performance of MCSA. Results: The experimental results showed that the performance of MCSA outperformed standard CSA and the existing literature methods. Conclusion: The MCSA provides the diversity by using the tournament selection scheme because it gives the opportunity to all solutions to participate in the selection process.

Quantum-inspired firefly algorithm integrated with cuckoo search for optimal path planning

2021 ◽  
pp. 2250018
Author(s):  
Harish Kundra ◽  
Wasim Khan ◽  
Meenakshi Malik ◽  
Kantilal Pitambar Rane ◽  
Rahul Neware ◽  
...  
Keyword(s):  
Path Planning ◽  
Firefly Algorithm ◽  
Satellite Images ◽  
Heuristic Algorithms ◽  
Optimization Problems ◽  
Optimal Path ◽  
Cuckoo Search ◽  
Google Earth ◽  
Solution Set ◽  
Optimal Path Planning

The firefly algorithm and cuckoo search are the meta-heuristic algorithms efficient to determine the solution for the searching and optimization problems. The current work proposes an integrated concept of quantum-inspired firefly algorithm with cuckoo search (IQFACS) that adapts both algorithms’ expedient attributes to optimize the solution set. In the IQFACS algorithm, the quantum-inspired firefly algorithm (QFA) ensures the diversification of fireflies-based generated solution set using the superstitions quantum states of the quantum computing concept. The cuckoo search (CS) algorithm uses the Lévy flight attribute to escape the QFA from the premature convergence and stagnation stage more effectively than the quantum principles. Here, the proposed algorithm is applied for the application of optimal path planning. Before using the proposed algorithm for path planning, the algorithm is tested on different optimization benchmark functions to determine the efficacy of the proposed IQFACS algorithm than the firefly algorithm (FA), CS, and hybrid FA and CS algorithm. Using the proposed IQFACS algorithm, path planning is performed on the satellite images with vegetation as the focused region. These satellite images are captured from Google Earth and belong to the different areas of India. Here, satellite images are converted into morphologically processed binary images and considered as maps for path planning. The path planning process is also executed with the FA, CS, and QFA algorithms. The performance of the proposed algorithm and other algorithms are accessed with the evaluation of simulation time and the number of cycles to attain the shortest path from defined source to destination. The error rate measure is also incorporated to analyze the overall performance of the proposed IQFACS algorithm over the other algorithms.

scholarly journals A novel meta-heuristic optimization algorithm based on cell division: Cell Division Optimizer

2021 ◽  
Author(s):  
Sehej Jain ◽  
Kusum Kumari Bharti
Keyword(s):  
Cell Division ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Cuckoo Search ◽  
Optimization Method ◽  
Global Optimum ◽  
Cellular Level ◽  
Engineering Problem ◽  
Grey Wolf Optimizer ◽  
Division Cell

Abstract A novel meta-heuristic algorithm named as the Cell Division Optimizer (CDO) is proposed. The proposed algorithm is inspired by the reproduction methods at the cellular level, which is formulated by the well-known cell division process known as mitosis and meiosis. In the proposed model Meiosis and Mitosis govern the exploration and exploitation aspects of the optimization algorithm, respectively. In the proposed method, the solutions are updated in two phases to achieve the global optimum solution. The proposed algorithm can be easily adopted to solve the combinatorial optimization method. To evaluate the proposed method, 50 well-known benchmark test functions and also 2 classical engineering optimization problems including 1 mechanical engineering problem and 1 electrical engineering problem are employed. The results of the proposed method are compared with the latest versions of state-of-the-art algorithms like Particle Swarm Optimization, Cuckoo Search, Grey Wolf Optimizer, FruitFly Optimization, Whale Optimizer, Water-Wave Optimizer and recently proposed variants of top-performing algorithms like SHADE (success history-based adaptive differential evolution) and CMAES (Covariance matrix adaptation evolution strategy). Moreover, the convergence speed of the proposed algorithm is better than the considered competitive methods in most cases.

Optimal broadcast scheduling method for VANETs: An adaptive discrete firefly approach

2020 ◽  
Vol 39 (6) ◽  
pp. 8125-8137
Author(s):  
Jackson J Christy ◽  
D Rekha ◽  
V Vijayakumar ◽  
Glaucio H.S. Carvalho
Keyword(s):  
Intelligent Transportation System ◽  
Search Algorithm ◽  
Mac Protocol ◽  
High Mobility ◽  
Optimal Solution ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Traffic Density ◽  
Scheduling Problem ◽  
Broadcast Scheduling

Vehicular Adhoc Networks (VANET) are thought-about as a mainstay in Intelligent Transportation System (ITS). For an efficient vehicular Adhoc network, broadcasting i.e. sharing a safety related message across all vehicles and infrastructure throughout the network is pivotal. Hence an efficient TDMA based MAC protocol for VANETs would serve the purpose of broadcast scheduling. At the same time, high mobility, influential traffic density, and an altering network topology makes it strenuous to form an efficient broadcast schedule. In this paper an evolutionary approach has been chosen to solve the broadcast scheduling problem in VANETs. The paper focusses on identifying an optimal solution with minimal TDMA frames and increased transmissions. These two parameters are the converging factor for the evolutionary algorithms employed. The proposed approach uses an Adaptive Discrete Firefly Algorithm (ADFA) for solving the Broadcast Scheduling Problem (BSP). The results are compared with traditional evolutionary approaches such as Genetic Algorithm and Cuckoo search algorithm. A mathematical analysis to find the probability of achieving a time slot is done using Markov Chain analysis.

scholarly journals Price and reimbursement of advanced therapeutic medicinal products in Europe: are assessment and appraisal diverging from expert recommendations?

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Virginia Ronco ◽  
Myriam Dilecce ◽  
Elena Lanati ◽  
Pier Luigi Canonico ◽  
Claudio Jommi
Keyword(s):  
Selection Process ◽  
Grey Literature ◽  
Primary Objective ◽  
Medicinal Products ◽  
Health Authorities ◽  
Advanced Therapy ◽  
Additional Funding ◽  
Managed Entry Agreements ◽  
R Process ◽  
Expert Recommendations

Abstract Background Advanced therapy medicinal products (ATMPs) represent an important cornerstone for innovation in healthcare. However, uncertainty on the value, the high average cost per patient and their one-shot nature has raised a debate on their assessment and appraisal process for pricing and reimbursement (P&R) purposes. This debate led experts providing for recommendations on this topic. Our primary objective is to investigate the ATMPs P&R process in the main five European countries and to understand if this process is consistent with published P&R expert recommendations. We also investigated the current ATMP pipelines to understand if future ATMPs will create challenges for their P&R process. Methods P&R framework for ATMPs in the European Major five (EU5) countries was investigated through a literature search on PubMed, institutional websites of National Health Authorities and grey literature. The ATMPs pipeline database was populated from a clinical trial database (clinicaltrials.gov), relying on inclusion and exclusion criteria retrieved from the literature. Results Reimbursement status of ATMPs is different across the EU5 countries, with the exception of CAR-Ts which are reimbursed in all countries. Standard P&R process in place for other medicinal products is extended to ATMPs, with the exception of some cases in Germany. List prices, where available, are high and, tend to be aligned across countries. Outcome-based Managed Entry Agreements (MEAs) have been extensively used for ATMPs. Extra-funds for hospitals managing ATMPs were provided only in Germany and, as additional fund per episode, in France. The accreditation process of hospitals for ATMPs management was in most countries managed by the national authorities. As far as ATMPs pipeline is concerned, ATMPs in development are mostly targeting non-rare diseases. Conclusions Expert recommendations for ATMPs P&R were partially applied: the role of outcome-based MEAs has increased and the selection process of the centres authorized to use these treatments has been enhanced; additional funding for ATMPs management to accredited centres has not been completely considered and annuity payment and broader perspective in cost considerations are far from being put in place. These recommendations should be considered for future P&R negotiations to pursue rational resource allocation and deal with budget constraints.

scholarly journals Privacy-Preserving Smart Road-Pricing System with Trustworthiness Evaluation in VANETs

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3658
Author(s):  
Qingfeng Zhu ◽  
Sai Ji ◽  
Jian Shen ◽  
Yongjun Ren
Keyword(s):  
Traffic Control ◽  
Traffic Management ◽  
Location Privacy ◽  
Vehicular Ad Hoc Networks ◽  
Intelligent Transportation System ◽  
Smart Cities ◽  
Security Analysis ◽  
Geographical Location ◽  
Experimental Simulation ◽  
Control Center

With the advanced development of the intelligent transportation system, vehicular ad hoc networks have been observed as an excellent technology for the development of intelligent traffic management in smart cities. Recently, researchers and industries have paid great attention to the smart road-tolling system. However, it is still a challenging task to ensure geographical location privacy of vehicles and prevent improper behavior of drivers at the same time. In this paper, a reliable road-tolling system with trustworthiness evaluation is proposed, which guarantees that vehicle location privacy is secure and prevents malicious vehicles from tolling violations at the same time. Vehicle route privacy information is encrypted and uploaded to nearby roadside units, which then forward it to the traffic control center for tolling. The traffic control center can compare data collected by roadside units and video surveillance cameras to analyze whether malicious vehicles have behaved incorrectly. Moreover, a trustworthiness evaluation is applied to comprehensively evaluate the multiple attributes of the vehicle to prevent improper behavior. Finally, security analysis and experimental simulation results show that the proposed scheme has better robustness compared with existing approaches.

scholarly journals A Self-Adaptive Cuckoo Search Algorithm Using a Machine Learning Technique

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1840
Author(s):  
Nicolás Caselli ◽  
Ricardo Soto ◽  
Broderick Crawford ◽  
Sergio Valdivia ◽  
Rodrigo Olivares
Keyword(s):  
Machine Learning ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Set Covering ◽  
Covering Problem ◽  
Parameter Configuration ◽  
Self Tuning ◽  
Problem Solvers

Metaheuristics are intelligent problem-solvers that have been very efficient in solving huge optimization problems for more than two decades. However, the main drawback of these solvers is the need for problem-dependent and complex parameter setting in order to reach good results. This paper presents a new cuckoo search algorithm able to self-adapt its configuration, particularly its population and the abandon probability. The self-tuning process is governed by using machine learning, where cluster analysis is employed to autonomously and properly compute the number of agents needed at each step of the solving process. The goal is to efficiently explore the space of possible solutions while alleviating human effort in parameter configuration. We illustrate interesting experimental results on the well-known set covering problem, where the proposed approach is able to compete against various state-of-the-art algorithms, achieving better results in one single run versus 20 different configurations. In addition, the result obtained is compared with similar hybrid bio-inspired algorithms illustrating interesting results for this proposal.

scholarly journals GMBO: Group Mean-Based Optimizer for Solving Various Optimization Problems

Mathematics ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 1190
Author(s):  
Mohammad Dehghani ◽  
Zeinab Montazeri ◽  
Štěpán Hubálovský
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Random Search ◽  
Optimization Algorithms ◽  
Gravitational Search Algorithm ◽  
Main Idea ◽  
Population Based ◽  
Grey Wolf Optimizer ◽  
Whale Optimization

There are many optimization problems in the different disciplines of science that must be solved using the appropriate method. Population-based optimization algorithms are one of the most efficient ways to solve various optimization problems. Population-based optimization algorithms are able to provide appropriate solutions to optimization problems based on a random search of the problem-solving space without the need for gradient and derivative information. In this paper, a new optimization algorithm called the Group Mean-Based Optimizer (GMBO) is presented; it can be applied to solve optimization problems in various fields of science. The main idea in designing the GMBO is to use more effectively the information of different members of the algorithm population based on two selected groups, with the titles of the good group and the bad group. Two new composite members are obtained by averaging each of these groups, which are used to update the population members. The various stages of the GMBO are described and mathematically modeled with the aim of being used to solve optimization problems. The performance of the GMBO in providing a suitable quasi-optimal solution on a set of 23 standard objective functions of different types of unimodal, high-dimensional multimodal, and fixed-dimensional multimodal is evaluated. In addition, the optimization results obtained from the proposed GMBO were compared with eight other widely used optimization algorithms, including the Marine Predators Algorithm (MPA), the Tunicate Swarm Algorithm (TSA), the Whale Optimization Algorithm (WOA), the Grey Wolf Optimizer (GWO), Teaching–Learning-Based Optimization (TLBO), the Gravitational Search Algorithm (GSA), Particle Swarm Optimization (PSO), and the Genetic Algorithm (GA). The optimization results indicated the acceptable performance of the proposed GMBO, and, based on the analysis and comparison of the results, it was determined that the GMBO is superior and much more competitive than the other eight algorithms.

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