Performance Of Evolutionary Algorithms Research Articles

Study on the performance and efficiency of multi-objective evolutionary algorithms for trimaran outrigger layout seakeeping optimization problem

Trimaran as a high-performance vessel, possesses excellent characteristics such as high speed, low wave-making resistance, and good seakeeping performance. Different from conventional monohull ship, the design of a trimaran requires additional consideration of the outrigger layout, as the position of two outriggers can affect its hydrodynamic performance. In this study, high-speed slender body potential flow theory (also named the 2D+t theory or the 2.5D method) was employed to calculate the heave and pitch motion in order to evaluate the seakeeping performance of trimarans with different outrigger layouts at varying speeds. Model experiments were subsequently conducted to validate the effectiveness of the 2.5D method in simulating the motion of trimarans under different outrigger layouts. Three multi-objective evolutionary algorithms, including Non-dominated sorting genetic algorithm II (NSGA-II), Non-dominated sorting genetic algorithm III (NSGA-III), and Multi-objective evolutionary algorithm based on decomposition (MOEA/D), as well as their applications in the optimization of trimaran outrigger layouts were introduced. Utilizing the 2.5D method based computational program as the solver, the efficiency and performance of these multi-objective evolutionary algorithms were compared. Discuss the advantages and disadvantages of three multi-objective evolutionary algorithms in solving trimaran outrigger layout optimization problem by comparing the optimization results from two aspects: optimization efficiency, and optimal solution performance. The results indicate that for optimization efficiency, the weighted sum approach based MOEA/D exhibits best optimization efficiency. NSGA-II and NSGA-III show a good advantage in terms of optimal solution performance, and compared to NSGA-II, NSGA-III can obtain more Pareto solutions.

A space sampling based large-scale many-objective evolutionary algorithm

Large-scale multiobjective optimization problems have attracted increasing attention in both engineering applications and scientific research. Academically, large-scale multiobjective problems involve hundreds or thousands of decision variables. Due to the large decision space, the performance of traditional multiobjective evolutionary algorithms decreases dramatically when dealing with large-scale multiobjective problems, especially many-objective problems. With this in mind, a space sampling based large-scale many-objective evolutionary algorithm (LSMaOEA) is proposed in this article. Specifically, a space sampling method is developed that alternately performs upper/lower-linkage sampling and individual-linkage sampling to sample a set of individuals in the decision space. An environmental selection strategy based on nondominated sorting and reference vector association is proposed. Thus, the proposed LSMaOEA can alleviate excessively dense sampling at boundaries and improve the diversity of existing space sampling based algorithms for large-scale many-objective problems. In the experiments, the proposed algorithm is assessed by comparing it with eight state-of-the-art multi/many-objective evolutionary algorithms. The evaluation is conducted using two popular indicators across nine challenging multiobjective optimization benchmark problems with up to 2000 decision variables. The extensive experimental results consistently reveal that the proposed algorithm outperforms all the compared algorithms.

MOAAA/D: a decomposition-based novel algorithm and a structural design application

When real-world engineering challenges are examined adequately, it becomes clear that multi-objective need to be optimized. Many engineering problems have been handled utilizing the decomposition-based optimization approach according to the literature. The performance of multi-objective evolutionary algorithms is highly dependent on the balance of convergence and diversity. Diversity and convergence are not appropriately balanced in the decomposition technique, as they are in many approaches, for real-world problems. A novel Multi-Objective Artificial Algae Algorithm based on Decomposition (MOAAA/D) is proposed in the paper to solve multi-objective structural problems. MOAAA/D is the first multi-objective algorithm that uses the decomposition-based method with the artificial algae algorithm. MOAAA/D, which successfully draws a graph on 24 benchmark functions within the area of two common metrics, also produced promising results in the structural design problem to which it was applied. To facilitate the design of the "rectangular reinforced concrete column" using MOAAA/D, a solution space was derived by optimizing the rebar ratio and the concrete quantity to be employed.

Multiobjective Evolutionary Component Effect on Algorithm Behaviour

The performance of multiobjective evolutionary algorithms (MOEAs) varies across problems, making it hard to develop new algorithms or apply existing ones to new problems. To simplify the development and application of new multiobjective algorithms, there has been an increasing interest in their automatic design from their components. These automatically designed metaheuristics can outperform their human-developed counterparts. However, it is still unknown what are the most influential components that lead to performance improvements. This study specifies a new methodology to investigate the effects of the final configuration of an automatically designed algorithm. We apply this methodology to a tuned Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) designed by the iterated racing (irace) configuration package on constrained problems of 3 groups: (1) analytical real-world problems, (2) analytical artificial problems and (3) simulated real-world. We then compare the impact of the algorithm components in terms of their Search Trajectory Networks (STNs), the diversity of the population, and the anytime hypervolume values. Looking at the objective space behavior, the MOEAs studied converged before half of the search to generally good HV values in the analytical artificial problems and the analytical real-world problems. For the simulated problems, the HV values are still improving at the end of the run. In terms of decision space behavior, we see a diverse set of the trajectories of the STNs in the analytical artificial problems. These trajectories are more similar and frequently reach optimal solutions in the other problems.

An adaptive reference vector guided many-objective optimization algorithm based on the pareto front density estimation

The performance of evolutionary algorithms using reference vectors to guide the evolution process mainly depends on the adaptive reference vector update strategy. In order to solve the challenging many-objective optimization problems with irregular Pareto fronts, this paper proposes an adaptive reference vector update strategy based on the Pareto front density estimation, which estimates the true Pareto front by finding sparse regions while ensuring the uniform distribution of reference vectors. In addition, an improved environmental selection strategy using the angle-based neighborhood density estimation has been proposed for estimating the neighborhood density to effectively guide the population evolution. On this basis, this paper proposes an adaptive reference vector guided many-objective optimization algorithm based on Pareto front density estimation (MaOEA-PDE). Experimental results on a large number of benchmark problems show MaOEA-PDE achieves better performance compared with some state-of-the-art algorithms in the literature.

Multi-Objective Automatic Clustering Algorithm Based on Evolutionary Multi-Tasking Optimization

Data mining technology is the process of extracting hidden knowledge and potentially useful information from a large number of incomplete, noisy, and random practical application data. The clustering algorithm based on multi-objective evolution has obvious advantages compared with the traditional single-objective method. In order to further improve the performance of evolutionary multi-objective clustering algorithms, this paper proposes a multi-objective automatic clustering model based on evolutionary multi-task optimization. Based on the multi-objective clustering algorithm that automatically determines the value of k, evolutionary multi-task optimization is introduced to deal with multiple clustering tasks simultaneously. A set of non-dominated solutions for clustering results is obtained by concurrently optimizing the overall deviation and connectivity index. Multi-task adjacency coding based on a locus adjacency graph was designed to encode the clustered data. Additionally, an evolutionary operator based on relevance learning was designed to facilitate the evolution of individuals within the population. It also facilitates information transfer between individuals with different tasks, effectively avoiding negative transfer. Finally, the proposed algorithm was applied to both artificial datasets and UCI datasets for testing. It was then compared with traditional clustering algorithms and other multi-objective clustering algorithms. The results verify the advantages of the proposed algorithm in clustering accuracy and algorithm convergence.

A two-stage direction-guided evolutionary algorithm for large-scale multiobjective optimization

Large-scale multiobjective optimization problems (LSMOPs) have exponential growth in the search space as the decision variables increase, and the vast search space poses a challenge to the performance of multiobjective evolutionary algorithms (MOEAs). Many current large-scale MOEAs need to consume a large amount of computational resources to get good performance. This paper proposes a two-stage direction-guided evolutionary algorithm for large-scale multiobjective optimization (LMOEA-S2D) to balance the performance and computational resource overhead. The algorithm exploits the Pareto-optimality property of domination and the diversity-preserving property of decomposition to optimize the performance in the two stages, respectively, and designs a corresponding direction-guided mechanism to improve search efficiency. LMOEA-S2D designs global direction search and local direction search in the domination-based stage for efficient exploitation to accelerate population convergence. To promote greater population diversity, a hybrid direction search was devised to aid diversity exploration in the decomposition-based stage, and this facilitates even distribution of candidate solutions across the Pareto optimal frontier. LMOEA-S2D is compared with five state-of-the-art large-scale MOEAs on some large-scale multiobjective test suites with 100 to 5,000 decision variables. The experimental results show that LMOEA-S2D significantly outperformed all compared algorithms under limited computational resources.

Application and evaluation of the evolutionary algorithms combined with conventional neural network to determine the building energy consumption of the residential sector

Residential uses a significant amount of energy; hence, encouraging sustainability and lessening environmental effects requires minimizing energy consumption in this sector. This study focuses on applying and evaluating evolutionary algorithms combined with conventional neural networks to predict building energy consumption in the residential sector. The primary objectives were to assess the performance of three evolutionary algorithms – Heap-Based Optimizer (HBO), Multiverse Optimizer (MVO), and Whale Optimization Algorithm (WOA) – in comparison to each other and to determine their effectiveness in predicting energy consumption. Each algorithm was integrated into the neural network framework to optimize the prediction model. Training and testing datasets were employed to evaluate the performance of the models. Two key statistical indices, Root Mean Square Error (RMSE) and R-squared (R2), were utilized to assess the accuracy of the predictions. The results of the evaluation demonstrated varying performances among the three evolutionary algorithms. MVO achieved the highest scores for both RMSE (48.55082 in training and 68.44517 in testing) and R2 (0.99184 in training and 0.98236 in testing) on both training and testing datasets, indicating superior predictive accuracy compared to HBO and WOA. These findings underscore the importance of algorithm selection in optimizing predictive models for energy consumption forecasting. Further research may explore hybrid approaches or parameter tuning to enhance the performance of evolutionary algorithms in this domain. Overall, this study contributes to advancing energy forecasting techniques, with potential implications for energy management and conservation efforts in the residential sector.

Tuning Reinforcement Learning Parameters for Cluster Selection to Enhance Evolutionary Algorithms.

The ability to find optimal molecular structures with desired properties is a popular challenge, with applications in areas such as drug discovery. Genetic algorithms are a common approach to global minima molecular searches due to their ability to search large regions of the energy landscape and decrease computational time via parallelization. In order to decrease the amount of unstable intermediate structures being produced and increase the overall efficiency of an evolutionary algorithm, clustering was introduced in multiple instances. However, there is little literature detailing the effects of differentiating the selection frequencies between clusters. In order to find a balance between exploration and exploitation in our genetic algorithm, we propose a system of clustering the starting population and choosing clusters for an evolutionary algorithm run via a dynamic probability that is dependent on the fitness of molecules generated by each cluster. We define four parameters, MFavOvrAll-A, MFavClus-B, NoNewFavClus-C, and Select-D, that correspond to a reward for producing the best structure overall, a reward for producing the best structure in its own cluster, a penalty for not producing the best structure, and a penalty based on the selection ratio of the cluster, respectively. A reward increases the probability of a cluster's future selection, while a penalty decreases it. In order to optimize these four parameters, we used a Gaussian distribution to approximate the evolutionary algorithm performance of each cluster and performed a grid search for different parameter combinations. Results show parameter MFavOvrAll-A (rewarding clusters for producing the best structure overall) and parameter Select-D (appearance penalty) have a significantly larger effect than parameters MFavClus-B and NoNewFavClus-C. In order to produce the most successful models, a balance between MFavOvrAll-A and Select-D must be made that reflects the exploitation vs exploration trade-off often seen in reinforcement learning algorithms. Results show that our reinforcement-learning-based method for selecting clusters outperforms an unclustered evolutionary algorithm for quinoline-like structure searches.

Handling the balance of operators in evolutionary algorithms through a weighted Hill Climbing approach

Evolutionary Algorithms (EAs) are a well-known domain within Artificial Intelligence. EAs have demonstrated their ability to tackle intricate optimization problems using evolutionary theory principles. However, balancing the dual exploration and exploitation processes remains a crucial concern. This paper introduces the Balanced Hill Climbing Weight Algorithm with Diversity (BHWEAD), an innovative approach that combines elements from classic Genetic Algorithm and Differential Evolution. BHWEAD uniquely employs the Hill Climbing local search to guide the influence of its operators, ensuring an optimal interplay between exploration and exploitation. Additionally, it incorporates a diversity control mechanism, resetting specific solutions to prevent premature convergence to suboptimal solutions. The main contribution of the BHWEAD is the mechanism that permits the balance of the exploration and exploitation stages; also, the incorporation of Hill Climbing permits a proper balance of the influence of the operators. Notice that the proposal can escape from suboptimal solutions using a diversity-based strategy. Tested against the CEC2017 benchmark functions in both 50 and 100 dimensions, BHWEAD outperformed 12 notable EAs, underscoring its potential for high-dimensional optimization problems. Besides, the proposed BHWEAD has also been tested over seven engineering problems, and the comparisons include some memetic algorithms., The paper provides additional insights into the algorithm’s design, conducts a comparative analysis, and identifies potential areas for improvement.

Adaptive multi-strategy particle swarm optimization for solving NP-hard optimization problems

Particle Swarm Optimization algorithm (PSO) has been widely utilized for addressing optimization problems due to its straightforward implementation and efficiency in tackling various test functions and engineering optimization problems. Nevertheless, PSO encounters issues like premature convergence and a lack of diversity, particularly when confronted with complex high-dimensional optimization tasks. In this study, we propose an enhanced version of the Island Model Particle Swarm Optimization (IMPSO), where island models are integrated into the PSO algorithm based on several migration strategies. The first contribution consists in applying a new selection and replacement strategies based on tabu search technique, while the second contribution consists in proposing a dynamic migration rate relying on the Biogeography-Based Optimization technique. To assess and validate the effectiveness of the proposed method, several unconstrained benchmark functions are applied. The obtained results confirm that the approach yield better performance than the old version of IMPSO for solving NP-hard optimization problems. Compared to the performance of other well-known evolutionary algorithms, the proposed approach is more efficient and effective.

Multi-objective optimization of dual resource integrated scheduling problem of production equipment and RGVs considering conflict-free routing.

In flexible job shop scheduling problem (FJSP), the collision of bidirectional rail guided vehicles (RGVs) directly affects RGVs scheduling, and it is closely coupled with the allocation of production equipment, which directly affects the production efficiency. In this problem, taking minimizing the maximum completion time of RGVs and minimizing the maximum completion time of products as multi-objectives a dual-resource integrated scheduling model of production equipment and RGVs considering conflict-free routing problem (CFRP) is proposed. To solve the model, a multi-objective improved discrete grey wolf optimizer (MOID-GWO) is designed. Further, the performance of popular multi-objective evolutionary algorithms (MOEAs) such as NSGA-Ⅱ, SPEA2 and MOPSO are selected for comparative test. The results show that, among 42 instances of different scales designed, 37, 34 and 28 instances in MOID-GWO are superior to the comparison algorithms in metrics of generational distance (GD), inverted GD (IGD) and Spread, respectively. Moreover, in metric of Convergence and Diversity (CD), the Pareto frontier (PF) obtained by MOID-GWO is closer to the optimal solution. Finally, taking the production process of a construction machinery equipment component as an example, the validity and feasibility of the model and algorithm are verified.

Nurse scheduling problem: Investigating the principles of operators in evolutionary algorithm for small size population

Nurse scheduling problem: Investigating the principles of operators in evolutionary algorithm for small size population

An adaptive many-objective evolutionary algorithm based on decomposition with two archives and an entropy trigger

ABSTRACT This article proposes two novel mechanisms to improve the performance of many-objective evolutionary algorithms based on Chebyshev scalarization. One mechanism improves the efficiency and effectiveness of the adaptation of the descent directions in criteria space, while the other ensures that extreme solutions are preserved. Weight adaptation via WS-transformation has shown promising results, but its performance is dependent on the choice of the start of the adaptation process. In order to overcome this limitation, in this article an efficient entropy-based trigger is proposed with fast calculation of the entropy that scales favourably with the number of dimensions. The novel entropy-based method is complemented by a dual-archiving mechanism that preserves extreme solutions. The dual-archiving strategy mitigates the possibility to discard those critical individuals whose loss affects the whole evolutionary process. The new algorithm proposed in this article (called aMOEA/D-2A-ET) is compared against a set of state-of-the-art MOEAs and shown to have competitive performance.

Competitive Decomposition-Based Multiobjective Architecture Search for the Dendritic Neural Model.

The dendritic neural model (DNM) is computationally faster than other machine-learning techniques, because its architecture can be implemented by using logic circuits and its calculations can be performed entirely in binary form. To further improve the computational speed, a straightforward approach is to generate a more concise architecture for the DNM. Actually, the architecture search is a large-scale multiobjective optimization problem (LSMOP), where a large number of parameters need to be set with the aim of optimizing accuracy and structural complexity simultaneously. However, the issues of irregular Pareto front, objective discontinuity, and population degeneration strongly limit the performances of conventional multiobjective evolutionary algorithms (MOEAs) on the specific problem. Therefore, a novel competitive decomposition-based MOEA is proposed in this study, which decomposes the original problem into several constrained subproblems, with neighboring subproblems sharing overlapping regions in the objective space. The solutions in the overlapping regions participate in environmental selection for the neighboring subproblems and then propagate the selection pressure throughout the entire population. Experimental results demonstrate that the proposed algorithm can possess a more powerful optimization ability than the state-of-the-art MOEAs. Furthermore, both the DNM itself and its hardware implementation can achieve very competitive classification performances when trained by the proposed algorithm, compared with numerous widely used machine-learning approaches.

Selection of Appropriate Portfolio Optimization Strategy

Portfolio optimization is a critical task in financial management, aiming to maximize expected returns while minimizing risk. This study compares the performance of Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Dynamic Programming (DP), and Differential Evolutionary Algorithm (DEA) in optimizing portfolios in the NIFTY 50 market. Using daily stock data from March 2023 to May 2023, we evaluate the algorithms based on performance metrics including the Sharpe ratio, expected return, volatility and Sortino ratio. Then an integrated multi-criteria decision making (MCDM) framework of Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) and Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS) methods has been used to compare the evolutionary algorithms for portfolio optimization. The results show that PSO outperforms the other algorithms in terms of Sharpe Ratio and Expected Return, while DEA exhibits the lowest portfolio risk. Furthermore, the efficient frontier analysis confirms PSO's ability to generate portfolios with higher expected returns at the same risk level. This research highlights the effectiveness of optimization algorithms in portfolio management and provides valuable insights for investors and portfolio managers in maximizing returns and managing risks.

On the behavior of parallel island models

Parallel island models (PIMs) are used to enhance the performance of evolutionary algorithms (EAs). In PIMs, each island executes an EA for evolving its local population, and periodically individuals migrate to neighborhoods synchronously or asynchronously. Neighborhoods are organized through a topology of communication, and migration policies guide individual exchange. This work explores migration policies over different communication topologies in synchronous and asynchronous PIMs to improve the speed-up and accuracy of genetic algorithms (GAs). The aim is to explain such models’ adequacy from a general perspective, attempting to answer questions such as the best way to implement GAs in PIMs. To reach this goal, the quality of the solutions and the running time provided by proposed PIMs are evaluated over four NP-hard problems of different combinatorial nature: reversal and translocation evolutionary distance, task mapping and scheduling, and N-Queens. The experiments show that tuning the parameters of the breeding cycle and migration policies is vital to guarantee that all proposed PIMs reach good speed-ups and more accurate solutions than the sequential GA. In addition, experiments ratify that synchronous models provide the best solutions, while asynchronous models deliver the best speed-ups. Finally, the results show that no model provides either better speed-up or accuracy in general since, as for sequential models, the nature of the problem define which would be the best-adapted PIM.

Cooperative-competitive two-stage game mechanism assisted many-objective evolutionary algorithm

It is critical to maintain significant convergence and diversity in many-objective optimization problems (MaOPs) for the performance of many-objective evolutionary algorithms (MaOEAs). However, some issues pose serious challenges to MaOEAs, such as the intensification of the conflict between convergence and diversity, and the lack of Pareto selection pressure. To address the above issues, we propose a cooperative-competitive two-stage game mechanism assisted many-objective evolutionary algorithms (MaOEA-GM). The algorithm is divided into two stages, such as competition and cooperative. In competitive game stage, a strategy pool is constructed, including angle penalty distance strategy and favorable convergence strategy. In addition, a new game utility function is designed to balance convergence and diversity. This method promotes the selection of genetically superior parents for inheritance, so that the population can quickly approach the true Pareto front. In cooperative game stage, individuals choose their preferred environmental selection mechanism by voting. The final scheme is determined using the criterion of minority obedience to the majority, thereby increasing the algorithm Pareto selection pressure. Experimental results demonstrate that compared with five advanced MaOEAs, The MaOEA-GM algorithm has not only preponderance in convergence and diversity indicators, but also higher competitiveness in solving MaOPs.

Manifold Interpolation for Large-Scale Multiobjective Optimization via Generative Adversarial Networks.

Large-scale multiobjective optimization problems (LSMOPs) are characterized as optimization problems involving hundreds or even thousands of decision variables and multiple conflicting objectives. To solve LSMOPs, some algorithms designed a variety of strategies to track Pareto-optimal solutions (POSs) by assuming that the distribution of POSs follows a low-dimensional manifold. However, traditional genetic operators for solving LSMOPs have some deficiencies in dealing with the manifold, which often results in poor diversity, local optima, and inefficient searches. In this work, a generative adversarial network (GAN)-based manifold interpolation framework is proposed to learn the manifold and generate high-quality solutions on the manifold, thereby improving the optimization performance of evolutionary algorithms. We compare the proposed approach with several state-of-the-art algorithms on various large-scale multiobjective benchmark functions. The experimental results demonstrate that significant improvements have been achieved by the proposed framework in solving LSMOPs.

A large-scale continuous optimization benchmark suite with versatile coupled heterogeneous modules

The complexity of the engineering design optimizations mainly sources from the large-scale nature of the problems, and the ever-increasing large-scale global optimization (LSGO) problems place high demands on the performance of evolutionary algorithms (EAs). To compare and analyze large-scale optimization algorithms, benchmarks that simulate the features from real-world large-scale optimization are desired. In this paper, we propose a novel large-scale continuous optimization benchmark suite, which includes 15 test functions with the modular structure. The proposed benchmark suite introduces two new features abstracted from the engineering design problems: (1) Heterogeneous design, i.e., the modules are significantly different in terms of difficulty and explicit expressions. (2) Versatile coupling, i.e., different coupling degrees and random coupling topologies of the benchmark are emphasized. Two typical cooperative coevolution frameworks and several state-of-the-art large-scale optimization algorithms are used for comparative studies on the proposed benchmark, and experimental results demonstrate that the proposed benchmark suite is very challenging.