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 Reinforcement Learning: A Survey

Reinforcement Learning (RL) has emerged as a highly effective technique for addressing various scientific and applied problems. Despite its success, certain complex tasks remain challenging to be addressed solely with a single model and algorithm. In response, ensemble reinforcement learning (ERL), a promising approach that combines the benefits of both RL and ensemble learning (EL), has gained widespread popularity. ERL leverages multiple models or training algorithms to comprehensively explore the problem space and possesses strong generalization capabilities. In this study, we present a comprehensive survey on ERL to provide readers with an overview of recent advances and challenges in the field. First, we introduce the background and motivation for ERL. Second, we analyze in detail the strategies that have been successfully applied in ERL, including model averaging, model selection, and model combination. Subsequently, we summarize the datasets and analyze algorithms used in relevant studies. Finally, we outline several open questions and discuss future research directions of ERL. By providing a guide for future scientific research and engineering applications, this survey contributes to the advancement of ERL.Comment: 42 page

A Pareto-based evolutionary algorithm using decomposition and truncation for dynamic multi-objective 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.Maintaining a balance between convergence and diversity of the population in the objective space has been widely recognized as the main challenge when solving problems with two or more conflicting objectives. This is added by another difficulty of tracking the Pareto optimal solutions set (POS) and/or the Pareto optimal front (POF) in dynamic scenarios. Confronting these two issues, this paper proposes a Pareto-based evolutionary algorithm using decomposition and truncation to address such dynamic multi-objective optimization problems (DMOPs). The proposed algorithm includes three contributions: a novel mating selection strategy, an efficient environmental selection technique and an effective dynamic response mechanism. The mating selection considers the decomposition-based method to select two promising mating parents with good diversity and convergence. The environmental selection presents a modified truncation method to preserve good diversity. The dynamic response mechanism is evoked to produce some solutions with good diversity and convergence whenever an environmental change is detected. In the experimental studies, a range of dynamic multi-objective benchmark problems with different characteristics were carried out to evaluate the performance of the proposed method. The experimental results demonstrate that the method is very competitive in terms of convergence and diversity, as well as in response speed to the changes, when compared with six other state-of-the-art methods

Solving dynamic multi-objective problems with an evolutionary multi-directional search approach

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.The challenge of solving dynamic multi-objective optimization problems is to effectively and efficiently trace the varying Pareto optimal front and/or Pareto optimal set. To this end, this paper proposes a multi-direction search strategy, aimed at finding the dynamic Pareto optimal front and/or Pareto optimal set as quickly and accurately as possible before the next environmental change occurs. The proposed method adopts a multi-directional search approach which mainly includes two parts: an improved local search and a global search. The first part uses individuals from the current population to produce solutions along each decision variable’s direction within a certain range and updates the population using the generated solutions. As a result, the first strategy enhances the convergence of the population. In part two, individuals are generated in a specific random method along every dimension’s orientation in the decision variable space, so as to achieve good diversity as well as guarantee the avoidance of local optimal solutions. The proposed algorithm is measured on several benchmark test suites with various dynamic characteristics and different difficulties. Experimental results show that this algorithm is very competitive in dealing with dynamic multi-objective optimization problems when compared with four state-of-the-art approaches

A prediction strategy based on decision variable analysis for dynamic multi-objective 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.Many multi-objective optimization problems in reality are dynamic, requiring the optimization algorithm to quickly track the moving optima after the environment changes. Therefore, response strategies are often used in dynamic multi-objective algorithms to find Pareto optimal. In this paper, we propose a hybrid prediction strategy based on the classification of decision variables, which consists of three steps. After detecting the environment change, the first step is to analyze the influence of each decision variable on individual convergence and distribution in the new environment. The second step is to adopt different prediction methods for different decision variables. Finally, adaptive selection is applied to the solution set generated in the first and second steps, and solutions with good convergence and diversity are selected to make the initial population more adaptable to the new environment. The prediction strategy can help the solution set converge while maintaining its diversity. The experimental results and performance show that the proposed algorithm is capable of significantly improving the dynamic optimization performance compared with five state-of-the-art evolutionary algorithms

Genome-Wide Association Study of Tacrolimus Pharmacokinetics Identifies Novel Single Nucleotide Polymorphisms in the Convalescence and Stabilization Periods of Post-transplant Liver Function

After liver transplantation, the liver function of a patient is gradually restored over a period of time that can be divided into a convalescence period (CP) and a stabilizing period (SP). The plasma concentration of tacrolimus, an immunosuppressant commonly used to prevent organ rejection, varies as a result of variations in its metabolism. The effects of genetic and clinical factors on the plasma concentration of tacrolimus appear to differ in the CP and SP. To establish a model explaining the variation in tacrolimus trough concentration between individuals in the CP and SP, we conducted a retrospective, single-center, discovery study of 115 pairs of patients (115 donors and 115 matched recipients) who had undergone liver transplantation. Donors and recipients were genotyped by a genome-wide association study (GWAS) using an exome chip. Novel exons were identified that influenced tacrolimus trough concentrations and were verified with bootstrap analysis. In donors, two single-nucleotide polymorphisms showed an effect on the CP (rs1927321, rs1057192) and four showed an effect on the SP (rs776746, rs2667662, rs7980521, rs4903096); in recipients, two single-nucleotide polymorphisms showed an effect in the SP (rs7828796, rs776746). Genetic factors played a crucial role in tacrolimus metabolism, accounting for 44.8% in the SP, which was higher than previously reported. In addition, we found that CYP3A5, which is known to affect the metabolism of tacrolimus, only influenced tacrolimus pharmacokinetics in the SP

A dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution

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.Many multi-objective optimization problems in the real world are dynamic, with objectives that conflict and change over time. These problems put higher demands on the algorithm’s convergence performance and the ability to respond to environmental changes. Confronting these two points, this paper proposes a dynamic multi-objective particle swarm optimization algorithm based on adversarial decomposition and neighborhood evolution (ADNEPSO). To overcome the instability of the traditional decomposition method for the changing Pareto optimal front (POF) shape, the proposed algorithm utilizes the complementary characteristics in the search area of the adversarial vector, and the two populations are alternately updated and co-evolved by adversarial search directions. Additionally, a novel particle update strategy is proposed to select promising neighborhood information to guide evolution and enhance diversity. To improve the ability to cope with environmental changes, an effective dynamic response mechanism is proposed, including three parts: archive set prediction, exploration of global optimal information, and retention of excellent particles to accelerate convergence to the Pareto optimal set (POS) in the new environment. The proposed algorithm is tested on a series of benchmark problems and compared to several state-of-the-art algorithms. The results show that ADNEPSO performed excellently in both convergence and diversity, and is highly competitive in dealing with dynamic problems

A dynamic multi-objective evolutionary algorithm based on intensity of environmental change

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.This paper proposes a novel evolutionary algorithm based on the intensity of environmental change (IEC) to effectively track the moving Pareto-optimal front (POF) or Pareto-optimal set (POS) in dynamic optimization. The IEC divides each individual into two parts according to the evolutionary information feedback from the POS in the current and former evolutionary environment when an environmental change is detected. Two parts, the micro-changing decision and macro-changing decision, are implemented upon different situations of decision components in order to build an efficient information exchange among dynamic environments. In addition, in our algorithm, if a new evolutionary environment is similar to its historical evolutionary environment, the history information will be used for reference to guide the search towards promising decision regions. In order to verify the availability of our idea, the IEC has been extensively compared with four state-of-the-art algorithms over a range of test suites with different features and difficulties. Experimental results show that the proposed IEC is promising

Photoredox-Catalyzed Acylation/Cyclization of 2-Isocyanobiaryls with Oxime Esters for the Synthesis of 6-Acyl Phenanthridines

An efficient acylation/cyclization reaction of 6-acyl phenanthridines with oxime esters using photoredox catalysis has been developed. This radical acyl transfer strategy enables a facile access to acyl-substituted phenanthridines with good yield and excellent selectivity. The developed method is redox neutral and has broad substrate scope and excellent functional group tolerance

A decision variable classification-based cooperative coevolutionary algorithm 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.This paper proposes a new decision variable classification-based cooperative coevolutionary algorithm, which uses the information of decision variable classification to guide the search process, for handling dynamic multiobjective problems. In particular, the decision variables are divided into two groups: convergence variables (CS) and diversity variables (DS), and different strategies are introduced to optimize these groups. Two kinds of subpopulations are used in the proposed algorithm, i.e., the subpopulations that represent DS and the sub-populations that represent CS. In the evolution process, the coevolution of DS and CS is carried out through genetic operators, and subpopulations of CS are gradually merged into DS, which is optimized in the global search space, based on an indicator to avoid becoming trapped in local optimum. Once a change is detected, a prediction method and a diversity introduction approach are adopted for these two kinds of variables to get a promising population with good diversity and convergence in the new environment. The proposed algorithm is tested on 16 benchmark dynamic multiobjective optimization problems, in comparison with state-of-the-art algorithms. Experimental results show that the proposed algorithm is very competitive for dynamic multiobjective optimization