Solving dynamic constrained optimisation problems using repair methods

It has been shown that (i) dynamic constrained optimisation problems (DCOPs), a very common class of problems in real-world applications, have some special characteristics that make them very different from unconstrained dynamic problems and stationary constrained problems and (ii) some existing dynamic optimisation (DO) and constraint handling (CH) algorithms might not work effectively in solving DCOPs. The ineffectiveness of existing algorithms in solving DCOPs, and the lack of algorithms specificaly designed for solving continuous DCOPs create an important gap in current research about DO. In this paper, we propose a set of new mechanisms to effectively handle dynamics in DCOPs and use them to develop new algorithms for solving DCOPs. The goal is to combine the advantages of DO and CH strategies while overcoming the drawbacks of these methods in solving DCOPs. To evaluate the performance of the new algorithms, we compare them against several representative DO and CH algorithms using a set of new performance measures and a set of 18 benchmark problems, which were designed to simulate the characteristics of DCOPs. The test results confirm the advantages of the newly proposed mechanisms and algorithms. Not only do they overcome all existing drawbacks and hence perform significantly better than the tested existing algorithms in solving DCOPs, they also perform equally to or better than these existing DO and CH algorithms in other groups of tested problems except in static problems. In this paper we also (i) carry out detailed analyses of how and why the newly proposed mechanisms/algorithms work better in DCOPs, (ii) investigate the contribution of each of the proposed mechanisms and (iii) study the influence of different parameter values on algorithm performance in solving DCOPs. These analyses reveal some interesting and counter-intuitive findings about the characteristics of DCOPs and the way we can solve DCOPs.