The problem of finding optimal configuration of automated/smart power distribution systems topology is an NP-hard combinatorial optimization problem. It becomes more complex when the time varying nature of loads is taken into account. In this paper, a systematic approach is proposed to determine an optimal long-term reconfiguration schedule. To solve the optimization problem, a novel adaptive fuzzy-based parallel genetic algorithm (GA) is proposed that employs the concept of parallel computing in identifying the optimal configuration of the network. The integration of fuzzy logic into the proposed method enhances the efficiency of the parallel GA by adaptively modifying the migration rates among different processors during the optimization process. A computationally efficient graph encoding method based on Dandelion coding strategy is developed, which automatically generates radial topologies and prevents the construction of infeasible radial networks in the optimization process. In order to consider the dynamic behavior of the load and reduce the load condition scenarios over the year under study, fuzzy C-mean clustering method is utilized. Finally, the performance of the proposed method is demonstrated on a 119-bus distribution network, and is compared with that of conventional single GA and conventional parallel GA.

Reconfiguration of Smart Distribution Systems With Time Varying Loads Using Parallel Computing

Fundamentals of genetic algorithms

Bayesian Network (BN) structure learning is a complex search problem, generally characterized by multimodality and epistasis. Genetic Algorithms (GAs) have been extensively used to pursue the BN structure learning task. This paper presents a new approach which incorporates the structural properties of the problem into GA mechanisms. The proposed approach uses a new recombination operator named Parent Set crossover, capable of reducing the disruptive action of the recombination process and enhancing its exploitative power. The new operator has been compared with a comprehensive set of other crossover operators as part of two genetic strategies: a canonical GA and a GA with an adaptive mutation scheme. All examined crossover operators were applied on both canonical and adaptive GAs and then compared in terms of various performance metrics. The experiments involve performance measures at the end of evolution as well as their convergence behavior across generations. The performance of the proposed method was also compared with the state-of-the-art non-evolutionary BN structure learning algorithms. Results show that the proposed recombination method enhances the algorithmic efficiency over a variety of test cases of different size.

https://dl.acm.org/doi/pdf/10.1145/3071178.3071240

The role of crossover operator in bayesian network structure learning performance: a comprehensive comparative study and new insights

Competitive optimization techniques have been developed to address the complexity of integrated water resources management (IWRM) modelling; however, model adaptation due to changing environments is still a challenge. In this paper we employ multi-variable techniques to increase confidence in model-driven decision-making scenarios. Here, water reservoir management was assessed using two evolutionary algorithm (EA) techniques, the epsilon-dominance-driven self-adaptive evolutionary algorithm (e-DSEA) and the Borg multi-objective evolutionary algorithm (MOEA). Many objective scenarios were evaluated to manage flood risk, hydropower generation, water supply, and release sequences over three decades. Computationally, the e-DSEA’s results are generally reliable, robust, effective and efficient when compared directly with the Borg MOEA but both provide decision support model outputs of value.

Assessment of water resources management strategy under different evolutionary optimization techniques

This paper addressed an important variant of two-dimensional cutting stock problem. The objective was not only to minimize trim loss, as in traditional cutting stock problems, but rather to minimiz...

An Innovative Genetic Algorithm for a Multi-Objective Optimization of Two-Dimensional Cutting-Stock Problem

algorithm is a search heuristic that mimics the natural process of evolution and it generates solution to a very complex NP-Hard problems. Genetic algorithm belongs to the class of evolutionary algorithms (EA) and it generates solution by using nature inspired techniques like selection, crossover and mutation. The performance of the genetic algorithm is mainly depends on the genetic operators. Genetic operators have the capability to maintain the genetic diversity. This paper mainly describes the available selection mechanisms as well as the crossover and the mutation operators. Keywordsalgorithm; Selection mechanism; Crossover; Mutation

/pdf/an-observational-analysis-of-genetic-operators-2tmthc5xr2.pdf

T. Geetha

Papers

An Observational Analysis of Genetic Operators