The main challenge in handling dynamic multi-objective optimization problems lies in the need for algorithms to accurately track Pareto-optimal solutions in constantly changing environments. Most existing predictionbased dynamic multi-objective evolutionary algorithms (DMOEAs) conduct prediction either in the decision space or the objective space alone, or apply the same prediction model to both spaces. However, such approaches may fail to fully capture the distinct change patterns of each space, especially under nonlinear and complex environmental dynamics, thereby limiting the effectiveness of these algorithms. Furthermore, when sampling methods are used to help the algorithm generate populations in new environments, a large number of sampled individuals can impose a significant computational burden due to the increased number of function evaluations. To address these limitations, this paper proposes a dynamic multi-objective evolutionary algorithm, namely DS-DMOEA, which efficiently adapts to environmental changes through a dual-space prediction strategy and a surrogate-based sampling strategy. The dual-space prediction strategy captures dynamic changes by employing a weight vector-based method in the objective space and a geodesic flow kernel method in the decision space. Simultaneously, the surrogate-based sampling strategy generates a high-quality sampling population by training surrogate models with information from similar historical environments. The predicted and sampled populations are then combined to form an initial population well-suited for the new environment. DS-DMOEA has been tested against nine state-of-the-art DMOEAs on 19 benchmark problems with three types of environmental change patterns. The experimental results validate the effectiveness of the proposed algorithm.

An evolutionary algorithm with neighborhood structure incorporating reinforcement learning for dual resource constrained flexible job shop scheduling problem with worker transportation fatigue

Smart design of overhead cranes using artificial intelligence and multi-objective optimization via evolutionary algorithms

Enhanced multi-scale TCN ensemble for component chain prediction using evolutionary algorithms

Balancing optimality and robustness is the key to solving expensive robust multiobjective optimization problems (ExRMOPs) by evolutionary algorithms. However, existing studies usually design algorithms based on either the average perspective or the worst perspective, overlooking the complementarity of these two perspectives-the former prefers optimality, whereas the latter prefers robustness. Therefore, this article proposes a Kriging-assisted evolutionary algorithm with dual perspectives and dual indicators (called KPI) to solve ExRMOPs. In KPI, we develop a dual-perspective aggregation function (DPAF) as the replaced objective to guide the evolutionary search. Specifically, in terms of each original objective, DPAF of each solution is defined as the weighted sum of the performance evaluated from the average perspective and the worst perspective. The weight used in DPAF is related to the stability level of the current population, enabling DPAF to adaptively balance optimality and robustness. In addition, we design a dual-indicator candidate selection strategy to identify high-quality candidates from the final population of the evolutionary search for expensive function evaluations. In this strategy, we first eliminate solutions with poor robust optimality by the proposed robust optimality indicator. Subsequently, based on the robust optimality indicator and a common diversity indicator, several solutions with good robust optimality and diversity are selected as candidates from the remaining solutions. Extensive experiments on two test suites and a real-world application verify the superiority of KPI.

Abstract To address issues such as premature convergence and entrapment in local optima during the optimization process of the Alpha Evolution (AE) algorithm, this study proposes an improved hybrid Alpha Evolution algorithm (HAE). The innovations of HAE are realized in three aspects: First, it adopts a two-dimensional cellular topology (Von Neumann-L5 neighborhood) to reconstruct individual interaction patterns, thereby maintaining population diversity via restricted information propagation; Second, a dynamic threshold-based hybrid search strategy is introduced to adaptively adjust the balance between global exploration and local exploitation; Third, a dynamic opposition-based learning mechanism is integrated, which generates high-quality opposite solutions using real-time search information to enhance the algorithm’s ability to escape local optima. Comparative experiments on the CEC2017 standard test functions and four typical engineering design problems show that the HAE algorithm outperforms the original AE algorithm significantly in terms of solution accuracy and convergence performance.

An efficient evolutionary feature selection algorithm with divide-and-conquer strategy for classification

Edge computing (EC) has emerged as a paradigm to support computation-intensive Internet of Things (IoT) applications by enabling task offloading to nearby servers. Despite its potential, the inherent heterogeneity of edge resources and the dynamic, unpredictable nature of task arrivals present significant challenges for designing and evaluating effective load balancing strategies. Traditional evaluation methods are limited as follows: physical testbeds lack scalability and flexibility, while abstract simulators often oversimplify network behavior, failing to capture realistic system dynamics. To address these limitations, we present an emulated dynamic edge computing framework (EDECF) designed for evaluating load balancing schemes in EC networks. First, we developed dedicated service models for each EC node within the EDECF and implemented them using the common open research emulator (CORE) platform, thereby providing a scalable, flexible, and realistic environment for testing optimization strategies. Second, we introduced a robust fitness function that explicitly models latency, queue stability, and fairness for metaheuristic-based load balancing under dynamic edge conditions. To assess its effectiveness, this function was incorporated and tested using the following methods: the particle swarm optimization, genetic algorithm, differential evolution and simulated annealing-based load balancing algorithms. In addition, baseline methods such as the round robin and shortest queue techniques were also deployed to demonstrate the framework’s capacity to facilitate rigorous analysis in heterogeneous and time-varying scenarios. Overall, results are presented to demonstrate EDECF’s capability to emulate realistic workloads, capture resource variability at the edge, and support comprehensive evaluation of algorithmic performance across diverse network settings. Thus, this work aims to establish a practical and extensible foundation for researchers and practitioners to design, test, and optimize load balancing strategies in EC environments.

Constrained multi-objective evolutionary algorithm with an infeasibility-aided strategy for the vehicle routing problem with time windows

A competition-driven two-phase evolutionary algorithm for constrained multi-objective optimization

A hyper-curvature balanced indicator and adaptive phase exploration co-driven evolutionary algorithm for many-objective optimization

Dual-space high-quality individual knowledge-driven surrogate-assisted multi-objective evolutionary algorithm with heterogeneous offspring generation

A reinforcement learning-based Synergistic Hybrid Evolutionary Algorithm for multi-angle shipboard photovoltaic system MPPT under dynamic navigational shading

Regularity model-driven large-scale multi-objective evolutionary algorithm based on dual-information offspring reproduction strategy

A novel Constraint Programming-Assisted evolutionary algorithm with deep Q-network for flexible job shop scheduling problem with robot constraints

A two-stage evolutionary algorithm with restart scheme for an integrated robot-task-scheduling and vehicle-dispatch-scheduling problem

A dual-population evolutionary algorithm with variable constraint guidance for constrained multi-objective optimization problems

Towards large-scale multi-objective feature selection: A two-stage evolutionary algorithm guided by dual feature weightings

A knowledge transfer-based membrane evolutionary algorithm for solving large-scale sorted waste collection problem with timeliness

A two-stage evolutionary algorithm based on hybrid penalty strategy and its application to multi-UAV path planning