Abstract
Dynamic multiobjective optimization problems (DMOPs) bring more challenges for multiobjective evolutionary algorithm (MOEA) due to its time-varying characteristic. To handle this kind of DMOPs, this paper presents a dynamic MOEA with multiple response strategies based on linear environment detection, called DMOEA-LEM. In this approach, different types of environmental changes are estimated and then the corresponding response strategies are activated to generate an efficient initial population for the new environment. DMOEA-LEM not only detects whether the environmental changes but also estimates the types of linear changes so that different prediction models can be selected to initialize the population when the environmental changes. To study the performance of DMOEA-LEM, a large number of test DMOPs are adopted and the experiments validate the advantages of our algorithm when compared to three state-of-the-art dynamic MOEAs.
Highlights
In the evolutionary computation community, the studies of static multiobjective optimization problems (MOPs) have been widely conducted during the recent decades, and there are a number of effective and efficient evolutionary algorithms for tackling static MOPs
In some practical engineering applications, it is found that some optimization problems are very complicated and need to be solved in a dynamic or uncertain environment, as their objective functions may change with the environment, which often exist in planning and scheduling problems [1,2,3,4], parameter optimization [5, 6], resource allocation [7, 8], and control system [9,10,11]. is kind of MOPs is often called dynamic multiobjective optimization problems (DMOPs), which can be defined in different aspects according to the nature of dynamics [12,13,14]. e solving of DMOPs needs to quickly search the Pareto-optimal set (PS) with good convergence and diversity when the environment is stable and has to efficiently obtain some promising solutions in new environment
When solving DMOPs in each new environment, evolution resource is very limited to get converged from an initial state, so an initial population close to the true Pareto-optimal front (PF) of the new environment is useful to speed up the convergence. ere are two challenges that often encounter when dealing with DMOPs [22]: one is to handle the conflicts in multiple objectives, while the other is to track their dynamism caused by the time-varied objective functions and constraints
Summary
In the evolutionary computation community, the studies of static multiobjective optimization problems (MOPs) have been widely conducted during the recent decades, and there are a number of effective and efficient evolutionary algorithms for tackling static MOPs. E environmental detection mechanism is used to determine whether the environment has changed, while the response mechanism aims to provide a new evolutionary direction for initial population. Based on the above considerations, we suggest a dynamic multiobjective evolutionary algorithm with multiple response strategies based on linear environment detection, called DMOEA-LEM. En, in the response mechanism, there are three prediction models involving the convergence and diversity of the population, and different dynamics are considered, in which one of them is activated to generate initial population. At is to say, each type of environmental change obtained by the change detection mechanism is associated with a prediction model to initialize population in new environment.
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