Medium-voltage distribution network expansion planning involves finding the most economical adjustments of both the capacity and the topology of the network such that no operational constraints are violated and the ex...
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ISBN:
(纸本)9783319116839;9783319116822
Medium-voltage distribution network expansion planning involves finding the most economical adjustments of both the capacity and the topology of the network such that no operational constraints are violated and the expected loads, that the expansion is planned for, can be supplied. This paper tackles this important real-world problem using realistic yet computationally feasible models and, for the first time, using two instances of the recently proposed class of Gene-pool Optimal Mixing evolutionary algorithms (GOMEAs) that have previously been shown to be a highly efficient integration of local search and genetic recombination, but only on standard benchmark problems. One GOMEA instance that we use employs linkage learning and one instance assumes no dependencies among problem variables. We also conduct experiments with a widely used traditional Genetic Algorithm (GA). Our results show that the favorable performance of GOMEA instances over traditional GAs extends to the real-world problem at hand. Moreover, the use of linkage learning is shown to further increase the algorithm's effectiveness in converging toward optimal solutions.
Multi-method and multi-operator evolutionary algorithms (EAs) have shown superiority to any single EAs with a single operator. To further improve the performance of such algorithms, in this research study, a united mu...
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ISBN:
(纸本)9781479914883
Multi-method and multi-operator evolutionary algorithms (EAs) have shown superiority to any single EAs with a single operator. To further improve the performance of such algorithms, in this research study, a united multi-operator EAs framework is proposed, in which two EAs, each with multiple search operators, are used. During the evolution process, the algorithm emphasizes on the best performing multi-operator EA, as well as the search operator. The proposed algorithm is tested on a well-known set of constrained problems with 10D and 30D. The results show that the proposed algorithm scales well and is superior to the-state-of-the-art algorithms, especially for the 30D test problems.
This paper introduces a novel two-step evolutionary algorithm (2-Step EA) for the procedural generation of dungeons in video games. Our approach is designed to address the complex challenge of generating dungeons that...
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ISBN:
(纸本)9798400704949
This paper introduces a novel two-step evolutionary algorithm (2-Step EA) for the procedural generation of dungeons in video games. Our approach is designed to address the complex challenge of generating dungeons that are not only structurally coherent and navigable with strategically placed keys and barriers. The algorithm divides the dungeon generation process in two phases: the initial phase focuses on the formation of dungeon layout considering room quantity and linear coefficient and the second phase deals with the allocation of keys and barriers within this structure. We compare our algorithm with existing methods, emphasizing efficiency and adherence to specified dungeon parameters. Our results show the effectiveness of the 2-Step EA in generating diverse and engaging dungeons. This research contributes to the field of procedural content generation in games, offering insights into the optimization of dungeon generation algorithms.
Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are *** a...
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Traditional large-scale multi-objective optimization algorithms(LSMOEAs)encounter difficulties when dealing with sparse large-scale multi-objective optimization problems(SLM-OPs)where most decision variables are *** a result,many algorithms use a two-layer encoding approach to optimize binary variable Mask and real variable Dec ***,existing optimizers often focus on locating non-zero variable posi-tions to optimize the binary variables ***,approxi-mating the sparse distribution of real Pareto optimal solutions does not necessarily mean that the objective function is *** data mining,it is common to mine frequent itemsets appear-ing together in a dataset to reveal the correlation between *** by this,we propose a novel two-layer encoding learning swarm optimizer based on frequent itemsets(TELSO)to address these *** mined the frequent terms of multiple particles with better target values to find mask combinations that can obtain better objective values for fast ***-mental results on five real-world problems and eight benchmark sets demonstrate that TELSO outperforms existing state-of-the-art sparse large-scale multi-objective evolutionary algorithms(SLMOEAs)in terms of performance and convergence speed.
Hyper-Heuristics is a recent area of research concerned with the automatic design of algorithms. In this paper we propose a grammar-based hyper-heuristic to automate the design of an evolutionary Algorithm component, ...
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ISBN:
(纸本)9783319116839;9783319116822
Hyper-Heuristics is a recent area of research concerned with the automatic design of algorithms. In this paper we propose a grammar-based hyper-heuristic to automate the design of an evolutionary Algorithm component, namely the parent selection mechanism. More precisely, we present a grammar that defines the number of individuals that should be selected, and how they should be chosen in order to adjust the selective pressure. Knapsack Problems are used to assess the capacity to evolve selection strategies. The results obtained show that the proposed approach is able to evolve general selection methods that are competitive with the ones usually described in the literature.
Reconfiguration and hardware implementation capabilities in reconfigurable computing (RC) systems make them more appropriate to recent computationally intensive applications. However, reaching optimal resource utiliza...
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Reconfiguration and hardware implementation capabilities in reconfigurable computing (RC) systems make them more appropriate to recent computationally intensive applications. However, reaching optimal resource utilization remained as one of the main challenges in these systems. In order to implement more tasks in each reconfiguration interval, several decisive factors such as execution time, data communication cost, and required hardware resources must be analyzed simultaneously. In this paper, we proposed a novel balanced-objective task selector combined with a genetic algorithm to efficiently pick up the tasks of an application and occupy the resources as more as possible. The multi-objective fitness function of this algorithm adequately partitions the input application and provides the desirable intra and inter-cluster characteristics. Moreover, a new chromosome encoding technique has been developed to prevent precedence constraint violation of invalid solutions by removing forbidden regions in the search space. We classified the input applications with topological features such as first level parallel tasks (FLPT) and critical path length (CPL) for comprehensive evaluation. Several experiments are performed on randomly generated and real-world Directed Acyclic Graphs (DAGs), and the results are more satisfying in DAGs with more FLPTs and shorter CPLs where up to 28.63% makespan and 29.3% resource utilization improvement have been achieved in comparison with previous methods.
multi-modal optimization problems (MMOPs) are pivotal in industrial production and scientific research. Unlike standard optimization problems, MMOPs aim to identify multiple global solutions, offering users a variety ...
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ISBN:
(纸本)9789819771806;9789819771813
multi-modal optimization problems (MMOPs) are pivotal in industrial production and scientific research. Unlike standard optimization problems, MMOPs aim to identify multiple global solutions, offering users a variety of optimal choices. However, traditional optimization algorithms often encounter difficulties when tackling MMOPs. To overcome this challenge, we propose a pretreatment mechanism based on individual distribution information, which is devised to enhance optimization algorithms' performance while preserving its convergence capability. We comprehensively evaluate our method's efficacy using 20 MMOPs from the CEC2013 benchmark suite, comparing it against the widely recognized "crowding method," a prevalent niching strategy. Our findings unequivocally showcase the effectiveness of the proposed mechanism in expediting MMOP optimization. Furthermore, we delve into an analysis elucidating the underlying reasons behind our proposal's effectiveness for MMOPs and discuss potential topics for future enhancements.
This study proposes to generalize the hybridization of evolutionary algorithm for solving large dimensional continuous global optimization problems. Inspired by various dual hybridizations being used, this paper propo...
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ISBN:
(纸本)9781479939756
This study proposes to generalize the hybridization of evolutionary algorithm for solving large dimensional continuous global optimization problems. Inspired by various dual hybridizations being used, this paper proposes hybrid evolutionary algorithms based on crossing over the FFA, PSO, BAT, ACO and GA algorithms. The main idea of the proposed method is to integrate the aforementioned algorithms by following best solutions of other algorithm using roulette wheel approach. The aim of the proposed hybrid algorithm was to enable problem solving using two or more evolutionary algorithms as is, without modification, besides effectively exploring and exploiting of the problem search space. Simulations for a series of benchmark test functions justify that an adroit hybridization of various evolutionary algorithms could yield a robust and efficient means of solving wide range of global optimization problems than the standalone evolutionary algorithms.
Inherent part of evolutionary algorithms that are based on Darwin theory of evolution and Mendel theory of genetic heritage, are random processes that are used in every evolutionary algorithm like genetic algorithms e...
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ISBN:
(数字)9783319071732
ISBN:
(纸本)9783319071725;9783319071732
Inherent part of evolutionary algorithms that are based on Darwin theory of evolution and Mendel theory of genetic heritage, are random processes that are used in every evolutionary algorithm like genetic algorithms etc. In this paper we present experiments (based on our previous) of selected evolutionary algorithms and test functions demonstrating impact of non-random generators on performance of the evolutionary algorithms. In our experiments we used differential evolution and SOMA algorithms with functions Griewangk and Rastrigin. We use n periodical deterministic processes (based on deterministic chaos principles) instead of pseudorandom number generators and compare performance of evolutionary algorithms powered by those processes and by pseudorandom number generators. Results presented here has to be understand like numerical demonstration rather than mathematical proofs. Our results (reported sooner and here) suggest hypothesis that certain class of deterministic processes can be used instead of random number generators without lowering the performance of evolutionary algorithms.
On-chip resistors are susceptible to temperature variations, affecting the performance of linear voltage-to-current (VI) conversion and vice versa. This paper introduces an approach to implement resistive networks tha...
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On-chip resistors are susceptible to temperature variations, affecting the performance of linear voltage-to-current (VI) conversion and vice versa. This paper introduces an approach to implement resistive networks that are highly immune to temperature variations across a wide range by combining complementary-to-absolute-temperature (CTAT) and proportional-to-absolute-temperature (PTAT) resistors existing in standard CMOS technology. The proposed resistive networks, aiming for linear VI conversion in voltage and current references (VCRs), yield ultra-low temperature coefficient (TC). Optimization is carried out using a multi-objective heuristic algorithm to find the optimal placement, TC and sizes of the elements within the final configuration. Post-layout simulation results in a standard 0.18-mu m CMOS process demonstrate the possibility of implementing sub-3 ppm/degrees C resistors across -40 similar to 120 degrees C temperature range, improving the prior art by more than 5x. A modern VCR configuration is implemented based on the proposed methodology, and simulation results verify the effectiveness of the modified approach in improving the accuracy of VI conversion.
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