21,223 research outputs found
Enhancing Cooperative Coevolution for Large Scale Optimization by Adaptively Constructing Surrogate Models
It has been shown that cooperative coevolution (CC) can effectively deal with
large scale optimization problems (LSOPs) through a divide-and-conquer
strategy. However, its performance is severely restricted by the current
context-vector-based sub-solution evaluation method since this method needs to
access the original high dimensional simulation model when evaluating each
sub-solution and thus requires many computation resources. To alleviate this
issue, this study proposes an adaptive surrogate model assisted CC framework.
This framework adaptively constructs surrogate models for different
sub-problems by fully considering their characteristics. For the single
dimensional sub-problems obtained through decomposition, accurate enough
surrogate models can be obtained and used to find out the optimal solutions of
the corresponding sub-problems directly. As for the nonseparable sub-problems,
the surrogate models are employed to evaluate the corresponding sub-solutions,
and the original simulation model is only adopted to reevaluate some good
sub-solutions selected by surrogate models. By these means, the computation
cost could be greatly reduced without significantly sacrificing evaluation
quality. Empirical studies on IEEE CEC 2010 benchmark functions show that the
concrete algorithm based on this framework is able to find much better
solutions than the conventional CC algorithms and a non-CC algorithm even with
much fewer computation resources.Comment: arXiv admin note: text overlap with arXiv:1802.0974
Limited Evaluation Cooperative Co-evolutionary Differential Evolution for Large-scale Neuroevolution
Many real-world control and classification tasks involve a large number of
features. When artificial neural networks (ANNs) are used for modeling these
tasks, the network architectures tend to be large. Neuroevolution is an
effective approach for optimizing ANNs; however, there are two bottlenecks that
make their application challenging in case of high-dimensional networks using
direct encoding. First, classic evolutionary algorithms tend not to scale well
for searching large parameter spaces; second, the network evaluation over a
large number of training instances is in general time-consuming. In this work,
we propose an approach called the Limited Evaluation Cooperative
Co-evolutionary Differential Evolution algorithm (LECCDE) to optimize
high-dimensional ANNs.
The proposed method aims to optimize the pre-synaptic weights of each
post-synaptic neuron in different subpopulations using a Cooperative
Co-evolutionary Differential Evolution algorithm, and employs a limited
evaluation scheme where fitness evaluation is performed on a relatively small
number of training instances based on fitness inheritance. We test LECCDE on
three datasets with various sizes, and our results show that cooperative
co-evolution significantly improves the test error comparing to standard
Differential Evolution, while the limited evaluation scheme facilitates a
significant reduction in computing time
A similarity-based cooperative co-evolutionary algorithm for dynamic interval multi-objective optimization problems
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Dynamic interval multi-objective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multi-objective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two sub-populations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, rgb0.00,0.00,0.00i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances rgb0.00,0.00,0.00as well as a multi-period portfolio selection problem and compared with five state-of-the-art evolutionary algorithms. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances
A Parallel Divide-and-Conquer based Evolutionary Algorithm for Large-scale Optimization
Large-scale optimization problems that involve thousands of decision
variables have extensively arisen from various industrial areas. As a powerful
optimization tool for many real-world applications, evolutionary algorithms
(EAs) fail to solve the emerging large-scale problems both effectively and
efficiently. In this paper, we propose a novel Divide-and-Conquer (DC) based EA
that can not only produce high-quality solution by solving sub-problems
separately, but also highly utilizes the power of parallel computing by solving
the sub-problems simultaneously. Existing DC-based EAs that were deemed to
enjoy the same advantages of the proposed algorithm, are shown to be
practically incompatible with the parallel computing scheme, unless some
trade-offs are made by compromising the solution quality.Comment: 12 pages, 0 figure
High-dimensional Black-box Optimization via Divide and Approximate Conquer
Divide and Conquer (DC) is conceptually well suited to high-dimensional
optimization by decomposing a problem into multiple small-scale sub-problems.
However, appealing performance can be seldom observed when the sub-problems are
interdependent. This paper suggests that the major difficulty of tackling
interdependent sub-problems lies in the precise evaluation of a partial
solution (to a sub-problem), which can be overwhelmingly costly and thus makes
sub-problems non-trivial to conquer. Thus, we propose an approximation
approach, named Divide and Approximate Conquer (DAC), which reduces the cost of
partial solution evaluation from exponential time to polynomial time.
Meanwhile, the convergence to the global optimum (of the original problem) is
still guaranteed. The effectiveness of DAC is demonstrated empirically on two
sets of non-separable high-dimensional problems.Comment: 7 pages, 2 figures, conferenc
Group Leaders Optimization Algorithm
We present a new global optimization algorithm in which the influence of the
leaders in social groups is used as an inspiration for the evolutionary
technique which is designed into a group architecture. To demonstrate the
efficiency of the method, a standard suite of single and multidimensional
optimization functions along with the energies and the geometric structures of
Lennard-Jones clusters are given as well as the application of the algorithm on
quantum circuit design problems. We show that as an improvement over previous
methods, the algorithm scales as N^2.5 for the Lennard-Jones clusters of
N-particles. In addition, an efficient circuit design is shown for two qubit
Grover search algorithm which is a quantum algorithm providing quadratic
speed-up over the classical counterpart
A Duality-Based Approach for Distributed Optimization with Coupling Constraints
In this paper we consider a distributed optimization scenario in which a set
of agents has to solve a convex optimization problem with separable cost
function, local constraint sets and a coupling inequality constraint. We
propose a novel distributed algorithm based on a relaxation of the primal
problem and an elegant exploration of duality theory. Despite its complex
derivation based on several duality steps, the distributed algorithm has a very
simple and intuitive structure. That is, each node solves a local version of
the original problem relaxation, and updates suitable dual variables. We prove
the algorithm correctness and show its effectiveness via numerical
computations
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