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Differential Evolution: A Survey of the State-of-the-Art
TLDR
Differential evolution (DE) is arguably one of the most powerful stochastic real-parameter optimization algorithms in current use. Expand
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Differential Evolution Using a Neighborhood-Based Mutation Operator
TLDR
Differential evolution (DE) is well known as a simple and efficient scheme for global optimization over continuous spaces. Expand
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Automatic Clustering Using an Improved Differential Evolution Algorithm
TLDR
Differential evolution (DE) has emerged as one of the fast, robust, and efficient global search heuristics of current interest. Expand
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Problem Definitions and Evaluation Criteria for CEC 2011 Competition on Testing Evolutionary Algorithms on Real World Optimization Problems
TLDR
Real world optimization problems can be used to evaluate the performance of different stochastic optimization algorithm. Expand
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An Adaptive Differential Evolution Algorithm With Novel Mutation and Crossover Strategies for Global Numerical Optimization
TLDR
We propose a new mutation strategy, a fitness- induced parent selection scheme for the binomial crossover of DE, and a simple but effective scheme of adapting two of its most important control parameters with an objective of achieving improved performance. Expand
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A Distance-Based Locally Informed Particle Swarm Model for Multimodal Optimization
TLDR
We propose a distance-based locally informed particle swarm (LIPS) optimizer, which eliminates the need to specify any niching parameter. Expand
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Recent advances in differential evolution - An updated survey
TLDR
Abstract Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary optimizers for the continuous parameter spaces in recent times. Expand
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Particle Swarm Optimization and Differential Evolution Algorithms: Technical Analysis, Applications and Hybridization Perspectives
TLDR
This article provides two recent algorithms for evolutionary optimization – well known as particle swarm optimization (PSO) and differential evolution (DE) and can take care of optimality on rough, discontinuous and multimodal surfaces. Expand
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Two improved differential evolution schemes for faster global search
TLDR
We present two new, improved variants of differential evolution, which are statistically significantly better on a seven-function test bed for the following performance measures: solution quality, time to find the solution, frequency of finding the solution. Expand
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Bacterial Foraging Optimization Algorithm: Theoretical Foundations, Analysis, and Applications
TLDR
Bacterial foraging optimization algorithm (BFOA) has been widely accepted as a global optimization algorithm of current interest for distributed optimization and control. Expand
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