Evolutionary Dynamic Multi-Objective Optimisation : A survey

Peer reviewedPostprin

An improved memory prediction strategy for dynamic multiobjective optimization

The file attached to this record is the author's final peer reviewed version.In evolutionary dynamic multiobjective optimization (EDMO), the memory strategy and prediction method are considered as effective and efficient methods. To handling dynamic multiobjective problems (DMOPs), this paper studies the behavior of environment change and tries to make use of the historical information appropriately. And then, this paper proposes an improved memory prediction model that uses the memory strategy to provide valuable information to the prediction model to predict the POS of the new environment more accurately. This memory prediction model is incorporated into a multiobjective evolutionary algorithm based on decomposition (MOEA/D). In particular, the resultant algorithm (MOEA/D-MP) adopts a sensor-based method to detect the environment change and find a similar one in history to reuse the information of it in the prediction process. The proposed algorithm is compared with several state-of-the-art dynamic multiobjective evolutionary algorithms (DMOEA) on six typical benchmark problems with different dynamic characteristics. Experimental results demonstrate that the proposed algorithm can effectively tackle DMOPs

An evolutionary dynamic multi-objective optimization algorithm based on center-point prediction and sub-population autonomous guidance

Dynamic multi-objective optimization problems (DMOPs) provide a challenge in that objectives conflict each other and change over time. In this paper, a hybrid approach based on prediction and autonomous guidance is proposed, which responds the environmental changes by generating a new population. According to the position of historical population, a part of the population is generated by predicting roughly and quickly. In addition, another part of the population is generated by autonomous guidance. A sub-population from current population evolves several generations independently, which guides the current population into the promising area. Compared with other three algorithms on a series of benchmark problems, the proposed algorithm is competitive in convergence and diversity. Empirical results indicate its superiority in dealing with dynamic environments

Ensemble Multi-Objective Biogeography-Based Optimization with Application to Automated Warehouse Scheduling

This paper proposes an ensemble multi-objective biogeography-based optimization (EMBBO) algorithm, which is inspired by ensemble learning, to solve the automated warehouse scheduling problem. First, a real-world automated warehouse scheduling problem is formulated as a constrained multi-objective optimization problem. Then EMBBO is formulated as a combination of several multi-objective biogeography-based optimization (MBBO) algorithms, including vector evaluated biogeography-based optimization (VEBBO), non-dominated sorting biogeography-based optimization (NSBBO), and niched Pareto biogeography-based optimization (NPBBO). Performance is tested on a set of 10 unconstrained multi-objective benchmark functions and 10 constrained multi-objective benchmark functions from the 2009 Congress on Evolutionary Computation (CEC), and compared with single constituent MBBO and CEC competition algorithms. Results show that EMBBO is better than its constituent algorithms, and among the best CEC competition algorithms, for the benchmark functions studied in this paper. Finally, EMBBO is successfully applied to the automated warehouse scheduling problem, and the results show that EMBBO is a competitive algorithm for automated warehouse scheduling

Ensemble Multi-Objective Biogeography-Based Optimization with Application to Automated Warehouse Scheduling

This paper proposes an ensemble multi-objective biogeography-based optimization (EMBBO) algorithm, which is inspired by ensemble learning, to solve the automated warehouse scheduling problem. First, a real-world automated warehouse scheduling problem is formulated as a constrained multi-objective optimization problem. Then EMBBO is formulated as a combination of several multi-objective biogeography-based optimization (MBBO) algorithms, including vector evaluated biogeography-based optimization (VEBBO), non-dominated sorting biogeography-based optimization (NSBBO), and niched Pareto biogeography-based optimization (NPBBO). Performance is tested on a set of 10 unconstrained multi-objective benchmark functions and 10 constrained multi-objective benchmark functions from the 2009 Congress on Evolutionary Computation (CEC), and compared with single constituent MBBO and CEC competition algorithms. Results show that EMBBO is better than its constituent algorithms, and among the best CEC competition algorithms, for the benchmark functions studied in this paper. Finally, EMBBO is successfully applied to the automated warehouse scheduling problem, and the results show that EMBBO is a competitive algorithm for automated warehouse scheduling

A Dynamic System Model of Biogeography-Based Optimization

We derive a dynamic system model for biogeography-based optimization (BBO) that is asymptotically exact as the population size approaches infinity. The states of the dynamic system are equal to the proportion of each individual in the population; therefore, the dimension of the dynamic system is equal to the search space cardinality of the optimization problem. The dynamic system model allows us to derive the proportion of each individual in the population for a given optimization problem using theory rather than simulation. The results of the dynamic system model are more precise than simulation, especially for individuals that are very unlikely to occur in the population. Since BBO is a generalization of a certain type of genetic algorithm with global uniform recombination (GAGUR), an additional contribution of our work is a dynamic system model for GAGUR. We verify our dynamic system models with simulation results. We also use the models to compare BBO, GAGUR, and a GA with single-point crossover (GASP) for some simple problems. We see that with small mutation rates, as are typically used in real-world problems, BBO generally results in better optimization results than GAs for the problems that we investigate

Hybrid of memory andprediction strategies for dynamic multiobjective optimization

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Dynamic multiobjective optimization problems (DMOPs) are characterized by a time-variant Pareto optimal front (PF) and/or Pareto optimal set (PS). To handle DMOPs, an algorithm should be able to track the movement of the PF/PS over time efficiently. In this paper, a novel dynamic multiobjective evolutionary algorithm (DMOEA) is proposed for solving DMOPs, which includes a hybrid of memory and prediction strategies (HMPS) and the multiobjective evolutionary algorithm based on decomposition (MOEA/D). In particular, the resultant algorithm (MOEA/D-HMPS) detects environmental changes and identifies the similarity of a change to the historical changes, based on which two different response strategies are applied. If a detected change is dissimilar to any historical changes, a differential prediction based on the previous two consecutive population centers is utilized to relocate the population individuals in the new environment; otherwise, a memory-based technique devised to predict the new locations of the population members is applied. Both response mechanisms mix a portion of existing solutions with randomly generated solutions to alleviate the effect of prediction errors caused by sharp or irregular changes. MOEA/D-HMPS was tested on 14 benchmark problems and compared with state-of-the-art DMOEAs. The experimental results demonstrate the efficiency of MOEA/D-HMPS in solving various DMOPs

A Dynamic System Model of Biogeography-Based Optimization

We derive a dynamic system model for biogeography-based optimization (BBO) that is asymptotically exact as the population size approaches infinity. The states of the dynamic system are equal to the proportion of each individual in the population; therefore, the dimension of the dynamic system is equal to the search space cardinality of the optimization problem. The dynamic system model allows us to derive the proportion of each individual in the population for a given optimization problem using theory rather than simulation. The results of the dynamic system model are more precise than simulation, especially for individuals that are very unlikely to occur in the population. Since BBO is a generalization of a certain type of genetic algorithm with global uniform recombination (GAGUR), an additional contribution of our work is a dynamic system model for GAGUR. We verify our dynamic system models with simulation results. We also use the models to compare BBO, GAGUR, and a GA with single-point crossover (GASP) for some simple problems. We see that with small mutation rates, as are typically used in real-world problems, BBO generally results in better optimization results than GAs for the problems that we investigate

A dynamic multi-objective evolutionary algorithm based on decision variable classification

The file attached to this record is the author's final peer reviewed version.In recent years, dynamic multi-objective optimization problems (DMOPs) have drawn increasing interest. Many dynamic multi-objective evolutionary algorithms (DMOEAs) have been put forward to solve DMOPs mainly by incorporating diversity introduction or prediction approaches with conventional multi-objective evolutionary algorithms. Maintaining good balance of population diversity and convergence is critical to the performance of DMOEAs. To address the above issue, a dynamic multi-objective evolutionary algorithm based on decision variable classification (DMOEA-DVC) is proposed in this study. DMOEA-DVC divides the decision variables into two and three different groups in static optimization and change response stages, respectively. In static optimization, two different crossover operators are used for the two decision variable groups to accelerate the convergence while maintaining good diversity. In change response, DMOEA-DVC reinitializes the three decision variable groups by maintenance, prediction, and diversity introduction strategies, respectively. DMOEA-DVC is compared with the other six state-of-the-art DMOEAs on 33 benchmark DMOPs. Experimental results demonstrate that the overall performance of the DMOEA-DVC is superior or comparable to that of the compared algorithms