Cooperative Coevolutionary Method 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.

Variants of bat algorithm for solving partitional clustering problems

Clustering is an exploratory data analysis technique that organize the data objects into clusters with optimal distance efficacy. In this work, a bat algorithm is considered to obtain optimal set of clusters. The bat algorithm is based on the echolocation feature of micro bats. Moreover, some improvements are proposed to overcome the shortcoming associated with bat algorithm like local optima, slow convergence, initial seed points and trade-off between local and global search mechanisms etc. An enhanced cooperative co-evolution method is proposed for addressing the initial seed points selection issue. The local optima issue is handled through neighbourhood search-based mechanism. The trade-off issue among local and global searches of bat algorithm is addressed through a modified elitist strategy. On the basis of aforementioned improvements, three variants (BA-C, BA-CN and BA-CNE) of bat algorithm is developed and efficacy of these variants is tested over twelve benchmark clustering datasets suing intra-cluster distance, accuracy and rand index parameters. Simulation results showed that BA-CNE variant achieves more effective clustering results as compared to BA-C, BA-CN and BA. The simulation results of BA-CNE are also compared with several existing clustering algorithms and two statistical tests are also applied to investigate the statistical difference among BA-CNE and other clustering algorithms. The simulation and statistical results confirmed that BA-CNE is an effective and robust algorithm for handling partitional clustering problems.

A cooperative coevolutionary method for optimizing random weight networks and its application for medical classification problems

A cooperative coevolutionary method for optimizing random weight networks and its application for medical classification problems

Parameter Identification of Induction Motor by Using Cooperative-Coevolution and a Nonlinear Estimator

Induction motors are one of the critical industrial drivers due to its simplicity, inexpensiveness, and high resistance. Such motors have a nonlinear model divided into two electrical and mechanical equations in terms of modeling. Knowing the values of electric parameters and mechanical moment of inertia is critically important for speed controlling and induction motors’ position. In many algorithms, electric parameters can be obtained by the locked rotor and unloaded tests, conducting these methods in laboratory would probably cost a lot of time and money. In this paper electrical parameters and moment of inertia are used approximately, without doing the above test by currents, voltages, and motor speed sampling in motor normal operation. This paper applies cooperative co-evolution method to remove certain costly tests that are required for induction motors. Two identification algorithms are suggested for all electrical parameters and moment of inertia. All inductances and resistances which are the two input parameters measured in electric equations using Cooperative-Coevolution algorithm. Mechanical model estimated the moment of inertia and load torque by using a nonlinear method based on Lyapunov. Computerized numerical simulations show that electric parameters, moment of inertia, and load torque were properly estimated by integrating the two smart and classic methods. The results show that the stator inductance error is about 1% and rotor inductance error is around 20%. Rotor and stator resistance error and self-Inductance is also less than one percent.

Particle swarm optimization with convergence speed controller for large-scale numerical optimization

Particle swarm optimization (PSO) has high convergence speed yet with its major drawback of premature convergence when solving large-scale optimization problems. We argue that it can be empowered by adaptively adjusting its convergence speed for the problems. In this paper, a convergence speed controller is proposed to improve the performance of PSO for large-scale optimization. As an additional operator of PSO, the controller is applied periodically and independently. It has two conditions and rules for adjusting the convergence speed of PSO, one for premature convergence and the other for slow convergence. The effectiveness of the PSO with convergence speed controller is evaluated by calculating the benchmark functions of CEC’2010. The numerical results indicate that the proposed controller helps PSO to keep a balance between convergence speed and swarm diversity during the optimization process. The results also support our argument that PSO can on average outperform other PSOs and cooperative coevolution methods for large-scale optimization when working with the convergence speed controller.

Heterogeneous Cooperative Co-Evolution Memetic Differential Evolution Algorithm for Big Data Optimization Problems

Evolutionary algorithms (EAs) have recently been suggested as a candidate for solving big data optimization problems that involve a very large number of variables and need to be analyzed in a short period of time. However, EAs face a scalability issue when dealing with big data problems. Moreover, the performance of EAs critically hinges on the utilized parameter values and operator types, thus it is impossible to design a single EA that can outperform all others in every problem instance. To address these challenges, we propose a heterogeneous framework that integrates a cooperative co-evolution method with various types of memetic algorithms. We use the cooperative co-evolution method to split the big problem into subproblems in order to increase the efficiency of the solving process. The subproblems are then solved using various heterogeneous memetic algorithms. The proposed heterogeneous framework adaptively assigns, for each solution, different operators, parameter values and a local search algorithm to efficiently explore and exploit the search space of the given problem instance. The performance of the proposed algorithm is assessed using the Big Data 2015 competition benchmark problems that contain data with and without noise. Experimental results demonstrate that the proposed algorithm, with the cooperative co-evolution method, performs better than without the cooperative co-evolution method. Furthermore, it obtained very competitive results for all tested instances, if not better, when compared to other algorithms using lower computational times.

Competition and Collaboration in Cooperative Coevolution of Elman Recurrent Neural Networks for Time-Series Prediction.

Collaboration enables weak species to survive in an environment where different species compete for limited resources. Cooperative coevolution (CC) is a nature-inspired optimization method that divides a problem into subcomponents and evolves them while genetically isolating them. Problem decomposition is an important aspect in using CC for neuroevolution. CC employs different problem decomposition methods to decompose the neural network training problem into subcomponents. Different problem decomposition methods have features that are helpful at different stages in the evolutionary process. Adaptation, collaboration, and competition are needed for CC, as multiple subpopulations are used to represent the problem. It is important to add collaboration and competition in CC. This paper presents a competitive CC method for training recurrent neural networks for chaotic time-series prediction. Two different instances of the competitive method are proposed that employs different problem decomposition methods to enforce island-based competition. The results show improvement in the performance of the proposed methods in most cases when compared with standalone CC and other methods from the literature.

Multi-objective Human-computer Co-operative Co-evolutionary Method Based on Non-dominated Sorting Strategy

Based on the human-computer cooperation ideas, a Human-Computer Multi-Objective Cooperative Co-Evolutionary Method (HCMCCM) is developed to solve the complex engineering layout problem, in which the multiobjective optimization idea is integrated to avoid the "flooding" phenomenon that occurs during the combination of the artificial solutions and the algorithm solutions. In the proposed HCMCCM, the artificial solutions expressed by unified encoding strings are incorporated together with the algorithms solutions to create new cooperative solutions based on the non-dominated sorting strategies. This kind of cooperation can make the artificial solutions and the algorithm solutions on an equal basis and integrate the artificial individual with the individual algorithms into a multi-objective trade-off. The numerical simulation results of the satellite layout problem show that the proposed method can combine the artificial solutions and the algorithm solutions effectively and provide a Pareto solution set for engineers to choose from.

A Cooperative Co-Evolutionary Method with Consistency Coordination Mechanisms and its Application to Complex Layout Problem

A Cooperative Co-Evolutionary Method with Consistency Coordination Mechanisms and its Application to Complex Layout Problem

Memetic cooperative coevolution of Elman recurrent neural networks

Cooperative coevolution decomposes an optimisation problem into subcomponents and collectively solves them using evolutionary algorithms. Memetic algorithms provides enhancement to evolutionary algorithms with local search. Recently, the incorporation of local search into a memetic cooperative coevolution method has shown to be efficient for training feedforward networks on pattern classification problems. This paper applies the memetic cooperative coevolution method for training recurrent neural networks on grammatical inference problems. The results show that the proposed method achieves better performance in terms of optimisation time and robustness.

Evolving team behaviors with specialization

This article evaluates Collective Neuro-Evolution (CONE), a cooperative co-evolutionary method for solving collective behavior tasks and increasing task performance via facilitating behavioral specialization in agent teams. Specialization is used as a problem solving mechanism, and its emergence is guided and regulated by CONE. CONE is comparatively evaluated with related methods in a simulated evolutionary robotics pursuit-evasion task. This task required multiple pursuer robots to cooperatively capture evader robots. Results indicate that CONE is appropriate for evolving specialized behaviors. The interaction of specialized behaviors produces behavioral heterogeneity in teams and collective prey capture behaviors that yield significantly higher performances compared to related methods.

Evolving behavioral specialization in robot teams to solve a collective construction task

This article comparatively tests three cooperative co-evolution methods for automated controller design in simulated robot teams. Collective Neuro-Evolution (CONE) co-evolves multiple robot controllers using emergent behavioral specialization in order to increase collective behavior task performance. CONE is comparatively evaluated with two related controller design methods in a collective construction task. The task requires robots to gather building blocks and assemble the blocks in specific sequences in order to build structures. Results indicate that for the team sizes tested, CONE yields a higher collective behavior task performance (comparative to related methods) as a consequence of its capability to evolve specialized behaviors.

Human-computer co-operative co-evolutionary method and its application to a satellite module layout design problem

AbstractThe layout design of a satellite module is a complex mechanical layout problem. Its main difficulties lie in combinatorial explosion of computational complexity, engineering complexity, and applicability in engineering practice. Inspired by the human-computer cooperation ideas, a human–computer co-operative co-evolutionary method for optimising layout design of a satellite module is developed. This method constructs the diversity reference set by using the diversity intelligence solutions (DIs) that are created by using the combinatorial operators of differential evolution (DE) and the blend crossover operator (BLX-a). During the co-evolution process of the presented method, the AIs, the DIs and the algorithm solutions are expressed by unified encoding strings and incorporated together to create new co-operative solutions. An instance of a satellite module layout design is presented to demonstrate the feasibility and effectiveness of the proposed method. Compared with the co-evolutionary approach and the all-at-once optimisation approaches, computational results show that the proposed method not only can produce better solutions, but also can better balance the conflicting objectives on the trade-off issues.