Emile Glorieux

Optimisation of interacting production stations using a Constructive Cooperative Coevolutionary approach

Simulation-based optimisation carries the burden of computationally expensive fitness calculations. It is very often used to tackle large-scale optimisation problems with a relatively high level of complexity. Therefore, it is of interest to have optimisation techniques dedicated to simulation-based optimisation. This paper proposes a simulation-based optimisation approach, called Constructive Cooperative Coevolutionary (C3) search procedure, to optimise the control of interacting production stations. An optimisation algorithm is embedded in the C3 search procedure to optimise subproblems separately. It includes a novel constructive heuristic that creates a feasible solution for the considered problem efficiently. It also incorporates an extended version of the existing cooperative coevolutionary method that can handle large-scale optimisation problems. Furthermore, this paper presents a case study considering a sheet metal press line as an example of interacting production stations. In this case study, the performance of the proposed C3 search procedure is evaluated and compared with other optimisation algorithms. This shows that the C3 search procedure is able to successfully optimise the press line within a given number of fitness calculations, outperforming existing algorithms. Also, it is shown that C3 can be embedded with either stochastic or deterministic optimisation algorithms, without sacrificing performance.

Improved Constructive Cooperative Coevolutionary Differential Evolution for Large-Scale Optimisation

The Differential Evolution (DE) algorithm is widely used for real-world global optimisation problems in many different domains. To improve DEs performance on large-scale optimisation problems, it has been combined with the Cooperative Coevolution (CCDE) algorithm. CCDE adopts a divide-and-conquer strategy to optimise smaller subcomponents separately instead of tackling the large-scale problem at once. DE then evolves a separate subpopulation for each subcomponent but there is cooperation between the subpopulations to co-adapt the individuals of the subpopulations with each other. The Constructive Cooperative Coevolution (C3DE) algorithm, previously proposed by the authors, is an extended version of CCDE that has a better performance on large-scale problems, interestingly also on non-separable problems. This paper proposes a new version, called the Improved Constructive Cooperative Coevolutionary Differential Evolution (C3iDE), which removes several limitations with the previous version. A novel element of C3iDE is the advanced initialisation of the subpopulations. C3iDE initially optimises the subpopulations in a partially co-adaptive fashion. During the initial optimisation of a subpopulation, only a subset of the other subcomponents is considered for the co-adaptation. This subset increases stepwise until all subcomponents are considered. The experimental evaluation of C3iDE on 36 high-dimensional benchmark functions (up to 1000 dimensions) shows an improved solution quality on large-scale global optimisation problems compared to CCDE and DE. The greediness of the co-adaptation with C3iDE is also investigated in this paper.

Constructive cooperative coevolution for large-scale global optimisation

This paper presents the Constructive Cooperative Coevolutionary (

Multi-objective constructive cooperative coevolutionary optimization of robotic press-line tending

\mathrm {C}^3

A Constructive Cooperative Coevolutionary Algorithm Applied to Press Line Optimisation

C3) algorithm, applied to continuous large-scale global optimisation problems. The novelty of

End-effector design optimisation and multi-robot motion planning for handling compliant parts

\mathrm {C}^3