Cooperative Coevolutionary Differential Evolution Algorithm Research Articles

Cooperative co-evolutionary differential evolution algorithm applied for parameters identification of lithium-ion batteries

Parameters identification of battery is a significant task for lithium-ion batteries. Some widely used techniques usually simplify the electrical circuit model (ECM) with non-linearity to a linear model or local linear model. However, by using such a methodology, the parameters in ECMs are not globally optimal, since the parameters may be not consistent at different linearized points. To address this issue, this paper proposed a cooperative co-evolution differential evolution (CCDE) algorithm to identify parameters of lithium-ion battery, without any linearization or pre-assumption. First, to describe the dynamic behaviors of battery, we presented a first-order RC equivalent circuit model ECM. Without making any approximation, improved Euler’s numerical method was utilized to solve the differential equations directly. Second, an optimizing objective function was built to minimize errors between the true and optimized terminal voltages. In that optimization model, parameters of battery (R0, RP and CP) and vOCV(t) at each sampling point were considered as variables to be optimized, resulting in a very high-dimension problem. Third, such an optimization problem was transformed into a large scale optimization problem (LSOP). Based on the character of parameters identification, we proposed a new m-decomposition method which is different from general grouping methods for benchmark functions and its corresponding differential evolution (DE) algorithm to solve this LSOP. Comprehensive experimental results demonstrated effectiveness of the proposed framework and methodology, compared with seven state-of-the-art cooperative co-evolution methods.

A Non-Dominated Sorting Cooperative Co-Evolutionary Differential Evolution Algorithm for Multi-Objective Layout Optimization

Different from traditional multi-objective evolutionary algorithms (MOEAS), multi-objective cooperative co-evolutionary algorithms (MOCCEAs) divide the decision variables into several subproblems to optimize. Solutions of each subproblem are evaluated by complete solutions formed through combining representative solutions from all subproblems. Therefore, the combination of representative solutions is a key issue in MOCCEAs. To improve the capability of MOCCEAs to complex multi-objective optimization problems, we propose a non-dominated sorting cooperative co-evolutionary differential evolution algorithm (NSCCDE). The proposed NSCCDE uses an external archive for storing complete solutions to establish a new collaboration mechanism, which forms a complete solution by combining collaborators from each subpopulation as well as from the external archive. On the one hand, the external archive is updated continuously through non-dominated sorting of complete solutions, which is conducive to speeding up the convergence. On the other hand, the external archive evolves itself through spatial dispersal and mutation operation to increase the diversity. The performance of proposed NSCCDE is then evaluated on a suite of satellite module layout optimization problem. Experimental results demonstrate that the proposed algorithm outperforms NSCCGA and NSGA- II.

Cooperative coevolutionary differential evolution with improved augmented Lagrangian to solve constrained optimisation problems

In constrained optimisation, the augmented Lagrangian method is considered as one of the most effective and efficient methods. This paper studies the behaviour of augmented Lagrangian function (ALF) in the solution space and then proposes an improved augmented Lagrangian method. We have shown that our proposed method can overcome some of the drawbacks of the conventional augmented Lagrangian method. With the improved augmented Lagrangian approach, this paper then proposes a cooperative coevolutionary differential evolution algorithm for solving constrained optimisation problems. The proposed algorithm is evaluated on a set of 24 well-known benchmark functions and five practical engineering problems. Experimental results demonstrate that the proposed algorithm outperforms the state-of-the-art algorithms with respect to solution quality as well as efficiency.

A Cooperative Coevolutionary Differential Evolution Algorithm with Adaptive Subcomponents

The performance of cooperative co-evolutionary (CC) algorithms for large-scale continuous optimization is significantly affected by the adopted decomposition of the search space. According to the literature, a typical decomposition in case of separable problems consists of adopting equally sized subcomponents for the whole optimization process (i.e. static decomposition). Such an approach is also often used for non-separable problems, together with a random-grouping strategy. More advanced methods try to determine the optimal size of subcomponents during the optimization process using reinforcement-learning techniques. However, the latter approaches are not always suitable in this case because of the non-stationary and history-dependent nature of the learning environment. This paper investigates a new CC algorithm, based on Differential Evolution, in which several decompositions are applied in parallel during short learning phases. The experimental results on a set of large-scale optimization problems show that the proposed method can lead to a reliable estimate of the suitability of each subcomponent size. Moreover, in some cases it outperforms the best static decomposition.

Dual-system Cooperative Coevolutionary Differential Evolution Algorithm for Solving Nonseparable Function Optimization

Dual-system Cooperative Coevolutionary Differential Evolution Algorithm for Solving Nonseparable Function Optimization

Parallel Dual-system Cooperative Co-evolutionary Differential Evolution Algorithm with Human-computer Cooperation for Multi-cabin Satellite Layout Optimization

Parallel Dual-system Cooperative Co-evolutionary Differential Evolution Algorithm with Human-computer Cooperation for Multi-cabin Satellite Layout Optimization

Opposition-based Cooperative Coevolutionary Differential Evolution Algorithm With Gaussian Mutation for Simplified Satellite Module Optimization

Opposition-based Cooperative Coevolutionary Differential Evolution Algorithm With Gaussian Mutation for Simplified Satellite Module Optimization