To overcome the disadvantages of traditional genetic algorithms, which easily fall to local optima, this paper proposes a hybrid genetic algorithm based on information entropy and game theory. First, ...a calculation of the species diversity of the initial population is conducted according to the information entropy by combining parallel genetic algorithms, including using the standard genetic algorithm (SGA), partial genetic algorithm (PGA) and syncretic hybrid genetic algorithm based on both SGA and PGA for evolutionary operations. Furthermore, with parallel nodes, complete-information game operations are implemented to achieve an optimum for the entire population based on the values of both the information entropy and the fitness of each subgroup population. Additionally, the Rosenbrock, Rastrigin and Schaffer functions are introduced to analyse the performance of different algorithms. The results show that compared with traditional genetic algorithms, the proposed algorithm performs better, with higher optimization ability, solution accuracy, and stability and a superior convergence rate.
Multi-modal multi-objective optimization problem (MMOPs) has attracted more and more attention in evolutionary computing recently. It is not easy to solve these problems using the existing ...evolutionary algorithms. The non-dominated solution sorting genetic algorithm (NSGA-II) has poor PS distribution and convergence. In this paper, an enhanced fast NSGA-II based on a special congestion strategy and adaptive crossover strategy, namely ASDNSGA-II is proposed. In the ASDNSGA-II, the strategy with a special congestion degree is used to improve the selection strategy. Then a new adaptive crossover strategy is designed by evaluating the advantages and disadvantages of the SBX crossover strategy with the ability to solve high dimensions and the BLX-α with the ability of Pareto solution to produce offspring solutions. These can ensure the generation of offspring solutions around individuals with large crowding degrees and balance the convergence and diversity of decision space and object space. It can improve PS distribution and convergence and maintain PF precision. Eight functions of MMF1-MMF8 from the CEC2020 are selected to prove the effectiveness of the ASDNSGA-II. By comparing several latest multi-modal multi-objective evolutionary algorithms, the results show that the ASDNSGA-II can effectively find the global Pareto solution set and improve the distribution and convergence of PS.
With the advent of smart vehicles, several new latency-critical and data-intensive applications are emerged in Vehicular Networks (VNs). Computation offloading has emerged as a viable option allowing ...to resort to the nearby edge servers for remote processing within a requested service latency requirement. Despite several advantages, computation offloading over resource-limited edge servers, together with vehicular mobility, is still a challenging problem to be solved. In particular, in order to avoid additional latency due to out-of-coverage operations, Vehicular Users (VUs) mobility introduces a bound on the amount of data to be offloaded towards nearby edge servers. Therefore, several approaches have been used for finding the correct amount of data to be offloaded. Among others, Federated Learning (FL) has been highlighted as one of the most promising solving techniques, given the data privacy concerns in VNs and limited communication resources. However, FL consumes resources during its operation and therefore incurs an additional burden on resource-constrained VUs. In this work, we aim to optimize the VN performance in terms of latency and energy consumption by considering both the FL and the computation offloading processes while selecting the proper number of FL iterations to be implemented. To this end, we first propose an FL-inspired distributed learning framework for computation offloading in VNs, and then develop a constrained optimization problem to jointly minimize the overall latency and the energy consumed. An evolutionary Genetic Algorithm is proposed for solving the problem in-hand and compared with some benchmarks. The simulation results show the effectiveness of the proposed approach in terms of latency and energy consumption.
Optimizing K-coverage of mobile WSNs Elhoseny, Mohamed; Tharwat, Alaa; Yuan, Xiaohui ...
Expert systems with applications,
02/2018, Letnik:
92
Journal Article
Recenzirano
Recently, Wireless Sensor Networks (WSNs) are widely used for monitoring and tracking applications. Sensor mobility adds extra flexibility and greatly expands the application space. Due to the ...limited energy and battery lifetime for each sensor, it can remain active only for a limited amount of time. To avoid the drawbacks of the classical coverage model, especially if a sensor died, K-coverage model requires at least k sensor nodes monitor any target to consider it covered. This paper proposed a new model that uses the Genetic Algorithm (GA) to optimize the coverage requirements in WSNs to provide continuous monitoring of specified targets for longest possible time with limited energy resources. Moreover, we allow sensor nodes to move to appropriate positions to collect environmental information. Our model is based on the continuous and variable speed movement of mobile sensors to keep all targets under their cover all times. To further prove that our proposed model is better than other related work, a set of experiments in different working environments and a comparison with the most related work are conducted. The improvement that our proposed method achieved regarding the network lifetime was in a range of 26%-41.3% using stationary nodes while it was in a range of 29.3%-45.7% using mobile nodes. In addition, the network throughput is improved in a range of 13%-17.6%. Moreover, the running time to form the network structure and switch between nodes' modes is reduced by 12%.
Optimization of production medium is required to maximize the metabolite yield. This can be achieved by using a wide range of techniques from classical "one-factor-at-a-time" to modern statistical ...and mathematical techniques, viz. artificial neural network (ANN), genetic algorithm (GA) etc. Every technique comes with its own advantages and disadvantages, and despite drawbacks some techniques are applied to obtain best results. Use of various optimization techniques in combination also provides the desirable results. In this article an attempt has been made to review the currently used media optimization techniques applied during fermentation process of metabolite production. Comparative analysis of the merits and demerits of various conventional as well as modern optimization techniques have been done and logical selection basis for the designing of fermentation medium has been given in the present review. Overall, this review will provide the rationale for the selection of suitable optimization technique for media designing employed during the fermentation process of metabolite production.
This paper presents a boost-multilevel inverter design with integrated battery energy storage system for standalone application. The inverter consists of modular switched-battery cells and a ...full-bridge. It is multifunctional and has two modes of operation: 1) the charging mode, which charges the battery bank and 2) the inverter mode, which supplies ac power to the load. This inverter topology requires significantly less power switches compared to conventional topology such as cascaded H-bridge multilevel inverter, leading to reduced size/cost and improved reliability. To selectively eliminate low-order harmonics and control the desired fundamental component, nonlinear system equations are represented in fitness function through the manipulation of modulation index and the genetic algorithm (GA) is employed to find the optimum switching angles. A seven-level inverter prototype is implemented and experimental results are provided to verify the feasibility of the proposed inverter design.
The import and export of crude oil are vastly affected by the economy of a developing country. It can be useful for the production of petroleum products. Likewise, the developing country, India is ...completely relying on the import of crude oil from Gulf countries. Thus, there is a need for optimization of routes and modes of transportation. This article presents a two-stage cost and time minimizing fuzzy business restricted multi−objective multi−index transportation problem, in which supplies, demands, and requirements are triangular fuzzy numbers. A business restricted constraints using binary variables are added in the developed supply chain of crude oil in India. The proposed model helps the decision-maker to choose the source country for the import of crude oil. The model is solved by using our proposed fuzzy non−dominated sorting genetic algorithm (NSGA)−II. The performance of the proposed algorithm is analyzed by using a simulation technique for uncertainty level <inline-formula> <tex-math notation="LaTeX">\alpha \in {0, 1} </tex-math></inline-formula>. Also, the Pareto decision space for formulated business restricted transportation problem is discussed using <inline-formula> <tex-math notation="LaTeX">\alpha - </tex-math></inline-formula>cut technique. For the superiority of the proposed methodology, we have implemented this on real-world case study viz., Daya case study. Based on the results we claim that the methodology is superior.
The main objective of this paper is to present a hybrid technique named as a PSO-GA for solving the constrained optimization problems. In this algorithm, particle swarm optimization (PSO) operates in ...the direction of improving the vector while the genetic algorithm (GA) has been used for modifying the decision vectors using genetic operators. The balance between the exploration and exploitation abilities have been further improved by incorporating the genetic operators, namely, crossover and mutation in PSO algorithm. The constraints defined in the problem are handled with the help of the parameter-free penalty function. The experimental results of constrained optimization problems are reported and compared with the typical approaches exist in the literature. As shown, the solutions obtained by the proposed approach are superior to those of existing best solutions reported in the literature. Furthermore, experimental results indicate that the proposed approach may yield better solutions to engineering problems than those obtained by using current algorithms.
We present a de novo discovery of an efficient catalyst of the Morita–Baylis–Hillman (MBH) reaction by searching chemical space for molecules that lower the estimated barrier of the rate‐determining ...step using a genetic algorithm (GA) starting from randomly selected tertiary amines. We identify 435 candidates, virtually all of which contain an azetidine N as the catalytically active site, which is discovered by the GA. Two molecules are selected for further study based on their predicted synthetic accessibility and have predicted rate‐determining barriers that are lower than that of a known catalyst. Azetidines have not been used as catalysts for the MBH reaction. One suggested azetidine is successfully synthesized and showed an eightfold increase in activity over a commonly used catalyst. We believe this is the first experimentally verified de novo discovery of an efficient catalyst using a generative model.
An efficient catalyst of the Morita–Baylis–Hillman reaction was discovered using a graph‐based genetic algorithm. The catalytic activity was experimentally verified by a kinetic study and the newly discovered catalyst outcompetes a widely used catalyst for this reaction.
•A novel solar micro Combined Cooling, Heating, and Power cycle is proposed.•Parametric study is presented to investigate the effects of various parameters.•Thermodynamic and thermoeconomic ...optimizations of the desired system are conducted.•Multi-objective optimization technique is applied using Genetic Algorithm.
This paper proposes a novel micro solar Combined Cooling, Heating and Power (CCHP) cycle integrated with Organic Rankine Cycle (ORC) for summer and winter seasons. The thermal storage tank is installed to correct the mismatch between the supply of the solar energy and the demand of thermal source consumed by the CCHP subsystem, thus the desired system could continuously and stably operate. The cycle is analyzed and optimized from the viewpoint of thermodynamics and thermoeconomics. For summer mode, the thermal efficiency, exergy efficiency and product cost rate are found to be 23.66%, 9.51% and 5114.5$/year, while for winter mode, these values are 48.45%, 13.76% and 5688.1$/year, respectively. Five key parameters, namely turbine inlet temperature, turbine inlet pressure, turbine back pressure, evaporator temperature and heater outlet temperature are selected as the decision variables to examine the performance of the overall system. The thermal efficiency, exergy efficiency and total product cost rate are selected as three objective functions and Genetic Algorithm (GA) is employed to find the final solutions to both single and multi-objective optimizations of the system. The results indicate that in summer, thermal efficiency, exergy efficiency and total product cost rate in optimum case are improved to 28%, 27% and 17%, respectively, while in winter, these values are 4%, 13% and 4%.
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