The study of optimization methods for reliability–redundancy allocation problems is a constantly changing *** algorithms are continually being designed on the basis of observations of nature,wildlife,and *** this pap...
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The study of optimization methods for reliability–redundancy allocation problems is a constantly changing *** algorithms are continually being designed on the basis of observations of nature,wildlife,and *** this paper,we review eight major evolutionary algorithms that emulate the behavior of civilization,ants,bees,fishes,and birds(i.e.,genetic algorithms,bee colony optimization,simulated annealing,particle swarm optimization,biogeography-based optimization,artificial immune system optimization,cuckoo algorithm and imperialist competitive algorithm).We evaluate the mathematical formulations and pseudo-codes of each algorithm and discuss how these apply to reliability–redundancy allocation *** from a literature survey show the best results found for series,series–parallel,bridge,and applied case problems(e.g.,overspeeding gas turbine benchmark).Review of literature from recent years indicates an extensive improvement in the algorithm reliability ***,this improvement has been difficult to achieve for high-reliability *** and future challenges in reliability–redundancy allocation problems optimization are also discussed in this paper.
Integrating renewable generation into the existing electricity grid to reduce Greenhouse Gas (GHG) emissions involves several challenges. These include, e.g., volatile generation and demand, and can be overcome by inc...
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ISBN:
(纸本)9798400701207
Integrating renewable generation into the existing electricity grid to reduce Greenhouse Gas (GHG) emissions involves several challenges. These include, e.g., volatile generation and demand, and can be overcome by increasing flexibility in the grid. One possibility to provide this flexibility is the optimized scheduling of Distributed Energy Resources (DERs). Such a scheduling task requires a powerful optimization algorithm, such as evolutionary algorithms (EAs). However, EAs can produce poor solution quality w.r.t. solution time when solving complex and large scale scheduling tasks of DERs. Hence, in our work, a concept for improving the EA optimization process for scheduling DERs is presented and evaluated. In this concept, Machine Learning (ML) algorithms learn from already found solutions to predict the optimization quality in advance. By this, the computational effort of the EA is directed to particularly difficult areas of the search space. This is achieved by dynamic interpretation and consequent interval length assignment of the solutions proposed by the EA. We evaluate our approach by comparing two experiments and show that our novel concept leads to a significant increase of the evaluated fitness by up to 9.4%.
This work examines how evolutionary Neural Architecture Search (NAS) algorithms can be improved by controlling the step size of the mutation of numerical parameters. The proposed NAS algorithms are based on F-DENSER, ...
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Mobile adhoc network (MANET) is an autonomous network, comprising several hosts which are linked to one another via wireless connections. Since the nodes in MANET are mobile in nature, clustering and routing become a ...
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Mobile adhoc network (MANET) is an autonomous network, comprising several hosts which are linked to one another via wireless connections. Since the nodes in MANET are mobile in nature, clustering and routing become a difficult task. Security is also a major issue which needs to be considered in the design of MANET protocols. The design of effective clustering and routing techniques helps to improve the network lifetime. Clustering and routing processes can be considered as an NP hard problem, which can be solved by evolutionary algorithms (EAs). With this motivation, this study presents an energy efficient clustering with secure routing protocol named EECSRP using hybrid EAs for MANET. The goal of the EECSRP technique is to cluster the nodes and elect optimal routes for energy efficient and reliable data transmission. The EECSRP technique involves two major stages. In the first stage, the cluster head selection and cluster construction process takes place using the niche mechanism with monarch butterfly optimization algorithm. Next, in the second stage, beta-hill climbing with grasshopper optimization algorithm is applied for optimal selection of routes in MANET. The performance validation of the proposed EECSRP model is assessed using NS2 tool and the results are inspected under several aspects. The experimental results show the promising performance of the EECSRP model over the other compared methods interms of different evaluation parameters.
We address the placement of emergency exits to facilitate evacuation from supermarket-like environments. Using a simulation-based approach, evolutionary algorithms are shown to provide good results compared to a greed...
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This paper uses evolutionary optimization algorithms to study the multi-objective optimization of mechanically stabilized earth (MSE) retaining walls. Five multi-objective optimization algorithms, including the non-do...
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This paper uses evolutionary optimization algorithms to study the multi-objective optimization of mechanically stabilized earth (MSE) retaining walls. Five multi-objective optimization algorithms, including the non-dominated sorting genetic algorithm II (NSGA-II), strength Pareto evolutionary algorithm II (SPEA-II), multi-objective particle swarm optimization (MOPSO), multi-objective multi-verse optimization (MVO), and Pareto envelope-based selection algorithm II (PESA-II), are applied to the design procedure. MSE wall design requires two major requirements: external stability and internal stability. In this study, the optimality criterion is to minimize cost and its trade-off with the factor of safety (FOS). To this end, two objectives are defined: (1) minimum cost, (2) maximum FOS. Three different strategies are considered for reinforcement combinations in the numerical simulations. Moreover, a sensitivity analysis was conducted on the variation of significant parameters, including backfill slope, wall height, horizontal earthquake coefficient, and surcharge load. The efficiency of the utilized algorithms was assessed through three well-known coverage set measures, diversity, and hypervolume. These measures were further examined using basic statistical measures (i.e., min, max, standard deviation) and the Friedman test with a 95% confidence level.
Final productive fitness is an a posteriori fitness estimate for evolutionary algorithms that takes into account the fitness of an individual's descendants. We use that metric in the context of surrogate-based evo...
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Multi-objective evolutionary algorithms have been shown to solve multi-objective optimization problems well and have been very widely used, but there are still drawbacks such as failure to develop sufficient environme...
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We analyze the performance of panmictic evolutionary algorithms in byzantine environments in which fitness can be computed by malicious agents. We measure the influence of the rate of unreliability of the environment,...
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ISBN:
(纸本)9798400701207
We analyze the performance of panmictic evolutionary algorithms in byzantine environments in which fitness can be computed by malicious agents. We measure the influence of the rate of unreliability of the environment, and the effect that a simple mechanism based on redundant computation can have on the results attained.
Game economy design significantly shapes the player experience and progression speed. Modern game economies are becoming increasingly complex and can be very sensitive to even minor numerical adjustments, which may ha...
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