6 research outputs found
A Novel Dual-Stage Evolutionary Algorithm for Finding Robust Solutions
In robust optimization problems, the magnitude of perturbations is relatively
small. Consequently, solutions within certain regions are less likely to
represent the robust optima when perturbations are introduced. Hence, a more
efficient search process would benefit from increased opportunities to explore
promising regions where global optima or good local optima are situated. In
this paper, we introduce a novel robust evolutionary algorithm named the
dual-stage robust evolutionary algorithm (DREA) aimed at discovering robust
solutions. DREA operates in two stages: the peak-detection stage and the robust
solution-searching stage. The primary objective of the peak-detection stage is
to identify peaks in the fitness landscape of the original optimization
problem. Conversely, the robust solution-searching stage focuses on swiftly
identifying the robust optimal solution using information obtained from the
peaks discovered in the initial stage. These two stages collectively enable the
proposed DREA to efficiently obtain the robust optimal solution for the
optimization problem. This approach achieves a balance between solution
optimality and robustness by separating the search processes for optimal and
robust optimal solutions. Experimental results demonstrate that DREA
significantly outperforms five state-of-the-art algorithms across 18 test
problems characterized by diverse complexities. Moreover, when evaluated on
higher-dimensional robust optimization problems (100- and 200-), DREA
also demonstrates superior performance compared to all five counterpart
algorithms
A convergence and diversity guided leader selection strategy for many-objective particle swarm optimization
Recently, particle swarm optimizer (PSO) is extended to solve many-objective optimization problems (MaOPs) and becomes a hot research topic in the field of evolutionary computation. Particularly, the leader particle selection (LPS) and the search direction used in a velocity update strategy are two crucial factors in PSOs. However, the LPS strategies for most existing PSOs are not so efficient in high-dimensional objective space, mainly due to the lack of convergence pressure or loss of diversity. In order to address these two issues and improve the performance of PSO in high-dimensional objective space, this paper proposes a convergence and diversity guided leader selection strategy for PSO, denoted as CDLS, in which different leader particles are adaptively selected for each particle based on its corresponding situation of convergence and diversity. In this way, a good tradeoff between the convergence and diversity can be achieved by CDLS. To verify the effectiveness of CDLS, it is embedded into the PSO search process of three well-known PSOs. Furthermore, a new variant of PSO combining with the CDLS strategy, namely PSO/CDLS, is also presented. The experimental results validate the superiority of our proposed CDLS strategy and the effectiveness of PSO/CDLS, when solving numerous MaOPs with regular and irregular Pareto fronts (PFs)
Process Knowledge-guided Autonomous Evolutionary Optimization for Constrained Multiobjective Problems
Various real-world problems can be attributed to constrained multi-objective optimization problems. Although there are various solution methods, it is still very challenging to automatically select efficient solving strategies for constrained multi-objective optimization problems. Given this, a process knowledge-guided constrained multi-objective autonomous evolutionary optimization method is proposed. Firstly, the effects of different solving strategies on population states are evaluated in the early evolutionary stage. Then, the mapping model of population states and solving strategies is established. Finally, the model recommends subsequent solving strategies based on the current population state. This method can be embedded into existing evolutionary algorithms, which can improve their performances to different degrees. The proposed method is applied to 41 benchmarks and 30 dispatch optimization problems of the integrated coal mine energy system. Experimental results verify the effectiveness and superiority of the proposed method in solving constrained multi-objective optimization problems.The National Key R&D Program of China, the National Natural Science Foundation of China, Shandong Provincial Natural Science Foundation, Fundamental Research Funds for the Central
Universities and the Open Research Project of The
Hubei Key Laboratory of Intelligent Geo-Information Processing.http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=4235hj2023Electrical, Electronic and Computer Engineerin
A New Two-Stage Evolutionary Algorithm for Many-Objective Optimization
© 1997-2012 IEEE. Convergence and diversity are interdependently handled during the evolutionary process by most existing many-objective evolutionary algorithms (MaOEAs). In such a design, the degraded performance of one would deteriorate the other, and only solutions with both are able to improve the performance of MaOEAs. Unfortunately, it is not easy to constantly maintain a population of solutions with both convergence and diversity. In this paper, an MaOEA based on two independent stages is proposed for effectively solving many-objective optimization problems (MaOPs), where the convergence and diversity are addressed in two independent and sequential stages. To achieve this, we first propose a nondominated dynamic weight aggregation method by using a genetic algorithm, which is capable of finding the Pareto-optimal solutions for MaOPs with concave, convex, linear and even mixed Pareto front shapes, and then these solutions are employed to learn the Pareto-optimal subspace for the convergence. Afterward, the diversity is addressed by solving a set of single-objective optimization problems with reference lines within the learned Pareto-optimal subspace. To evaluate the performance of the proposed algorithm, a series of experiments are conducted against six state-of-The-Art MaOEAs on benchmark test problems. The results show the significantly improved performance of the proposed algorithm over the peer competitors. In addition, the proposed algorithm can focus directly on a chosen part of the objective space if the preference area is known beforehand. Furthermore, the proposed algorithm can also be used to effectively find the nadir points