Evolutionary Dynamic Multi-objective Optimization via Regression Transfer Learning

Many real-world optimization problems involve multiple objectives, constraints, and parameters that may change over time. These problems are often called dynamic multiobjective optimization problems (DMOPs). The difficulty in solving DMOPs is the need to track the changing Pareto-optimal front efficiently and accurately. It is known that transfer learning (TL)-based methods have the advantage of reusing experiences obtained from past computational processes to improve the quality of current solutions. However, existing TL-based methods are generally computationally intensive and thus time consuming. This article proposes a new memory-driven manifold TL-based evolutionary algorithm for dynamic multiobjective optimization (MMTL-DMOEA). The method combines the mechanism of memory to preserve the best individuals from the past with the feature of manifold TL to predict the optimal individuals at the new instance during the evolution. The elites of these individuals obtained from both past experience and future prediction will then constitute as the initial population in the optimization process. This strategy significantly improves the quality of solutions at the initial stage and reduces the computational cost required in existing methods. Different benchmark problems are used to validate the proposed algorithm and the simulation results are compared with state-of-the-art dynamic multiobjective optimization algorithms (DMOAs). The results show that our approach is capable of improving the computational speed by two orders of magnitude while achieving a better quality of solutions than existing methods.

This paper investigates the use of additional-objective for solving multi-objective optimization problems in dynamic environment. A number of authors proposed the use of additional-objective for maintaining diversity in static single-objective optimization task; in this paper the work is extended to dealing with the dynamic multi-objective optimization problems. Individual diversity is used as an additional-objective, and a new efficient multi-objective optimization algorithm is proposed. Experimental results clearly indicate that the performance of the proposed algorithm is gratifying.

Many problems in the real-world have more than one objective, with at least two objectives in conflict with one another. In addition, at least one objective changes over time. These kinds of problems are called dynamic multi-objective optimisation problems (DMOOPs). Studies have shown that both the quantum particle swarm optimisation (QPSO) and charged particle swarm optimisation (CPSO) algorithms perform well in dynamic environments, since they maintain swarm diversity. Therefore, this paper investigates the effect of using either QPSOs or CPSOs in the sub-swarms of the dynamic vector-evaluated particle swarm optimisation (DVEPSO) algorithm. These DVEPSO variations are then compared against the default DVEPSO algorithm that uses gbest PSOs and DVEPSO using heterogeneous PSOs that contain both charged and quantum particles. Furthermore, all of the aforementioned DVEPSO configurations are compared against the dynamic multi-objective optimisation (DMOPSO) algorithm that was the winning algorithm of a comprehensive comparative study of dynamic multi-objective optimisation algorithms. The results indicate that charged and quantum particles improve the performance of DVEPSO, especially for DMOOPs with a deceptive POF and DMOOPs with a non-linear POS.

Dynamic multi-objective optimization problems (DMOPs) have become a research hotspot in engineering optimization, because their objective functions, constraints, or parameters may change over time, while quickly and accurately tracking the changing Pareto optimal set (POS) during the optimization process. Therefore, solving dynamic multi-objective optimization problems presents great challenges. In recent years, transfer learning has been proved to be one of the effective means to solve dynamic multi-objective optimization problems. However, this paper proposes a new transfer learning method based on clustering difference to solve DMOPs (TCD-DMOEA). Different from the existing methods, it uses the clustering difference strategy to optimize the population quality and reduce the data difference between the target domain and the source domain. On this basis, transfer learning technology is used to accelerate the construction of initialization population. The advantage of the TCD-DMOEA method is that it reduces the possibility of negative transfer and improves the performance of the algorithm by improving the similarity between the source domain and the target domain. Experimental results show that compared with several advanced dynamic multi-objective optimization algorithms based on different benchmark problems, the proposed TCD-DMOEA method can significantly improve the quality of the solution and the convergence speed.

Dynamic multi-objective optimization based on membrane computing for control of time-varying unstable plants

Dynamic multiobjective optimization problems (DMOPs) aim to optimize multiple (often conflicting) objectives that are changing over time. Recently, there are a number of promising algorithms proposed based on transfer learning methods to solve DMOPs. However, it is very challenging to reduce the negative effect in transfer learning and find more effective transferred solutions. To fill this research gap, this article proposes a clustering-based transfer (CBT) learning method to solve DMOPs. When the environment changes, two novel operations (clustering-based selection (CBS) and CBT) are used to guide knowledge transfer. Specifically, CBS aims to find a population with nondominated solutions and dominated solutions as the training data for the new environment. Then, CBT further collects the previous Pareto-optimal solutions and some noise solutions as the training data for the previous environment. Two training data sets from different environments are, respectively, divided into multiple clusters and transfer learning is conducted on two similar clusters with high probability to reduce the negative effect, which can train an accurate prediction model to identify the promising solutions for the new environment. Empirical studies have been conducted on 14 benchmark DMOPs and one real-life path planning problem of unmanned air/ground vehicles, which validate the effectiveness of our proposed method. Especially, our method can significantly reduce negative transfer on 12 out of 14 cases when compared with direct transfer learning.

Many real-world optimization problems have dynamic multiple objectives and constrains, such problems are called dynamic multi-objective optimization problems (DMOPs). Although many dynamic multi-objective evolutionary algorithms (DMOEAs) have been proposed to solve DMOPs, how to effectively track the optimal solutions in dynamic environments is still a major challenge for dynamic multi-objective optimization. Two classical DMOEAs, population prediction strategy (PPS) and transfer learning based DMOEA (Tr-DMOEA) are validated to have great performance because they integrate machine learning mechanism for optimization. However, there are still some disadvantages in both algorithms. In this paper, we propose a combined algorithm to make up the respective disadvantages of PPS and Tr-DMOEA. Our algorithm retains the prediction method of PPS considering sufficient historical information. Then, we improve the prediction strategy in Tr-DMOEA to further modify the solutions provided by PPS. These modified solutions finally construct the initial population for optimization in the new environment. The experiment results indicate that our algorithm has the overall best performance comparing with PPS and Tr-DMOEA on the test problems.

A change severity degree-based dynamic multi-objective optimization algorithm with adaptive response strategy

A decision variable classification-based cooperative coevolutionary algorithm for dynamic multiobjective optimization

This paper presents a new algorithm, called steady-state and generational evolutionary algorithm, which combines the fast and steadily tracking ability of steady-state algorithms and good diversity preservation of generational algorithms, for handling dynamic multiobjective optimization. Unlike most existing approaches for dynamic multiobjective optimization, the proposed algorithm detects environmental changes and responds to them in a steady-state manner. If a change is detected, it reuses a portion of outdated solutions with good distribution and relocates a number of solutions close to the new Pareto front based on the information collected from previous environments and the new environment. This way, the algorithm can quickly adapt to changing environments and thus is expected to provide a good tracking ability. The proposed algorithm is tested on a number of bi- and three-objective benchmark problems with different dynamic characteristics and difficulties. Experimental results show that the proposed algorithm is very competitive for dynamic multiobjective optimization in comparison with state-of-the-art methods.

Distributed satellite system (DSS) is a novel concept in satellite communication field. The system provides a flexible space scenario for multi-service network with guaranteed quality of service (QoS). To make different kinds of satellites to accomplish complex mission collaboratively, the resource must be properly allocated among different satellites with different requests. This paper investigates the downlink power allocation problem in DSS. Considering the high dynamics and the multi-constrains of the system, a dynamic multi-objective approach called constellation-level power allocation based on the immune clonal algorithm for dynamic multi-objective optimization (ICADMO) is proposed. The comparison and analysis of the performance between the proposed approach and other algorithms are carried out through the simulation results.

A new prediction strategy for dynamic multi-objective optimization using Gaussian Mixture Model

Performance measures for dynamic multi-objective optimisation algorithms

Multi-objective path planning in complex three-dimensional environments is a key challenge for improving the application efficiency of unmanned aerial vehicles in rescue, logistics and other fields. This paper proposes a dynamic multi-objective ant colony optimization algorithm to address the problems of traditional ant colony algorithms such as getting stuck in local optimum, slow convergence and difficulty in adapting to dynamic environments. This paper systematically constructs a multi-objective model that integrates path length, height constraints and threat avoidance, and designs a hierarchical pheromone update strategy based on path quality and a hybrid heuristic function for environmental adaptation to balance global exploration and local optimization capabilities. Simulation experiments show that compared with traditional ant colony, particle swarm and other algorithms, this method has significant improvements in path length, smoothness and computational efficiency, especially showing better obstacle avoidance success rate and real-time re-planning ability in high dynamic scenarios. This method effectively enhances the intelligent decision-making level of unmanned aerial vehicles in complex environments and has practical application value for improving the reliability of emergency response and logistics distribution.

The ultimate goal of the Internet of Things (IoT) is to provide ubiquitous services. To achieve this goal, many challenges remain to be addressed. Inspired from the cooperative mechanisms between multiple systems in the human being, this paper proposes a bio-inspired self-learning coevolutionary algorithm (BSCA) for dynamic multiobjective optimization of IoT services to reduce energy consumption and service time. BSCA consists of three layers. The first layer is composed of multiple subpopulations evolving cooperatively to obtain diverse Pareto fronts. Based on the solutions obtained by the first layer, the second layer aims to further increase the diversity of solutions. The third layer refines the solutions found in the second layer by adopting an adaptive gradient refinement search strategy and a dynamic optimization method to cope with changing concurrent multiple service requests, thereby effectively improving the accuracy of solutions. Experiments on agricultural IoT services in the presence of dynamic requests under different distributions are performed based on two service-providing strategies, i.e., single service and collaborative service. The simulation results demonstrate that BSCA performs better than four existing algorithms on IoT services, in particular for high-dimensional problems.