Evolutionary Multiobjective Optimization Algorithms Research Articles

Promising boundaries explore and resource allocation evolutionary algorithm for constrained multiobjective optimization

Promising boundaries explore and resource allocation evolutionary algorithm for constrained multiobjective optimization

Two-Stage Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization

Two-Stage Archive Evolutionary Algorithm for Constrained Multi-Objective Optimization

Critical vector based evolutionary algorithm for large-scale multi-objective optimization

Critical vector based evolutionary algorithm for large-scale multi-objective optimization

Identifier spaces for representing Pareto-optimal solutions in multi-objective optimization and decision-making

A multi-objective optimization task is not complete without a decision-making activity before, during, or after Pareto-optimal (PO) solutions are found. Evolutionary multi-objective optimization (EMO) algorithms, proposed in early nineties, are able to find multiple well-diversified near-PO solutions. Recent evolutionary many-objective optimization (EMaO) studies solve problems having more than three conflicting objectives using ideal-point-based reference vectors (RVs) on the objective space to guide the search and find diverse PO solutions for two- to 20-objective problems. In certain nonlinear problems, PO points guided by ideal-point-based RVs are not uniform on the PO front. This has caused EMaO researchers to develop other RVs suitable for finding a better distribution. We further argue that decision-makers (DMs) may want to visualize a well-distributed set of PO solutions on a different decision-making (identifier) space, other than the objective space, convenient to their decision-making preference. This article presents and compares six different identifier spaces, motivated from a decision-making perspective, based on ideal-point RVs, nadir-point RVs, projection RVs, pseudo-weight vectors, angle vectors, and RadViz coordinates. Advantages and disadvantages of these identifier spaces are laid out for optimization and decision-making purposes by implementing each on a suitable EMaO algorithm and applying them to standard test problems. The use of multiple identifier spaces simultaneously on a single EMO simulation is also executed for DMs to have a compromise distribution of PO solutions. The choice of one or more identifier spaces within EMO algorithms allows a generic, flexible and practical for addressing optimization and decision-making tasks together.

Surrogate-assisted decomposition multi-objective evolutionary algorithm for parameters optimization in polyester fiber polymerization process

Surrogate-assisted decomposition multi-objective evolutionary algorithm for parameters optimization in polyester fiber polymerization process

Multiobjective Day‐Ahead Scheduling of Reconfigurable‐Based Microgrids Through Electric Vehicles and Demand Response Integration

Nowadays, as the demand for plug‐in electric vehicles in microgrids is growing, there are various challenges that the network must face, including providing adequate electricity, addressing environmental concerns, and rescheduling the microgrid. In order to overcome these challenges, this paper introduces a novel multiobjective optimization model where the first objective is to minimize the total operation cost of the microgrid and the second objective is to maximize the reliability index by reducing the amount of system energy not supplied. Because of these two compromising objectives, the evolutionary multiobjective seagull optimization algorithm is utilized to find the best local solutions. In this regard, integrated plug‐in electric vehicles and demand response programs are used to smooth distribution locational marginal pricing. Furthermore, the effect of the system’s various configurations is analyzed in the suggested method to smooth the amount of distribution locational marginal prices in comparison to the initial case. Two case studies including modified IEEE 33‐bus and 69‐bus distribution networks are applied to evaluate the efficiency of the proposed approach.

Division-selection transfer learning for prediction based dynamic multi-objective optimization

Dynamic multi-objective optimization problems (DMOPs) are challenging as they require capturing the Pareto optimal front (POF) and Pareto optimal set (POS) during the optimization process. In recent years, transfer learning (TL) has emerged the empirical knowledge and is an effective approach to solve DMOPs. However, negative transfer can occur when the transfer method is not suitable for the transfer task. It may deviate the search path and seriously reduce the efficiency, so how to reduce the occurrence of negative transfer to save the running time of dynamic multi-objective evolutionary algorithm (DMOEA) is an important issue to be addressed. A division-selection transfer learning evolutionary algorithm for dynamic multi-objective optimization (DST-DMOEA) is designed towards this aim. Specifically, individuals with high Spearman correlation are relatively stable in different environments, selecting them to train the Support Vector Regression (SVR) model ensures a more accurate capture of solution features, predicting the objective values of historical solutions based on the model, and thus divide historical solutions into elite and non-elite solutions. Subsequently, for the elite solutions, individual TL that incorporates local information for optimization and transfer is used, while the non-elite solutions are handled with manifold TL method to obtain the overall data distribution and understand the internal structure. Then, merge the predicted individuals generated by two parts of TL will constitute as the initial population in the optimization process. Compared with other algorithms, the initial solution of DST-DMOEA is closer to the real POF, effectively reducing negative transfer. In addition, in 51 test instances, DST-DMOEA has shown superior performance in over 30 instances.

Assessment of evolutionary multi-objective optimisation algorithms for thermoplastic injection moulding in aluminium moulds

In order for a plastic part manufactured by the injection moulding process to be of the desired quality, it is necessary to maintain control of the process parameters. To increase productivity and reduce defects in parts, various studies have been carried out to propose and evaluate methods for determining injection parameters, which are based on the use of experimental planning techniques as well as the application of optimisation algorithms. This work proposes an experimental design with 6 injection parameters (mould temperature, melt temperature, injection time, percentage of volume filled, packing time and packing pressure) to determine the initial search space for the NSGA-II and MOEA/D optimisation algorithms in relation to the use of aluminium moulds. This procedure was used via a computer code described in the PYTHON programming language to determine the appropriate injection parameters for minimising part warpage and injection pressure. The results showed that an increase in compaction pressure is associated with a reduction in warpage and vice versa. It was observed that the NSGA-II algorithm showed a greater diversity of points on the approximate Pareto frontier compared to the MOEA/D algorithm. In the case of the proposed study, MOEA/D presented a more viable solution, determining a lower packing pressure, less warping of the part and a shorter cycle time. The NSGA-II algorithm required around 4 hours more computational time than MOEA/D.

Efficiency multi-agent model assisted Moea/D algorithm for optimization design for building taking into account annual energy consumption and annual user discomfort hours

Recently, with the continuous consumption of energy, building energy conservation has been popular in the energy field. In response to the high computational cost, slow convergence speed, and low accuracy of existing optimization design methods for building energy efficiency, this study first built a multi-objective optimization model for building energy efficiency on the ground of the annual energy consumption of buildings and the quantity of uncomfortable hours for users. Then it introduces a multi-agent model auxiliary mechanism to improve the decomposition based multi-objective evolutionary optimization algorithm, and then solves the multi-objective optimization model for building energy efficiency. In order to select the optimal decision variable of the algorithm, the decision parameters were analyzed and found that the performance was optimal when the number of samples, aggregation number and base model were set to 25.3 and 20. The improved multi-objective evolutionary optimization algorithm on the ground of decomposition has average supervolume and running time values of 32416.13 and 1774.58 seconds under office buildings, and 7899.13 and 3616.96 seconds under residential buildings, respectively. In addition, the annual user discomfort time of office buildings is 555.28h, which is lower than other comparison algorithms. In summary, the optimal performance of the algorithm when the decision variable is set to 25.3 and 20. The algorithm proposed by the research institute has superior performance and has certain application value in selecting the optimal solution for building energy-saving design.

Multiple surrogates-assisted evolutionary algorithm for high-dimensional expensive multi-objective optimization with adaptive diffusion map

Multiple surrogates-assisted evolutionary algorithm for high-dimensional expensive multi-objective optimization with adaptive diffusion map

Multi-Objective Evolutionary Framework for Layout and Operational Optimization of a Multi-Body Wave Energy Converter

As global energy demand continues to rise, the focus has increasingly shifted toward renewable energy sources. In this context, multi-body Wave Energy Converters (WECs) stand out due to their superior power capture efficiency and enhanced survivability, facilitated by their operational flexibility. Nevertheless, the complexity associated with deploying multi-body WECs in arrays has constrained extensive research in this domain. This study introduces an optimization strategy for the layout design and control of multi-body WECs aimed at efficiently harnessing wave energy. Utilizing Evolutionary Multi-Objective Optimization (EMO) algorithms, this framework optimizes the arrangement and operational parameters of WEC arrays, specifically tailored for the marine conditions off the coast of Oman. The research encompasses two primary optimization phases: layout optimization, which seeks an optimal balance between power production and the separation distance between WEC devices, and operational control optimization, which adjusts WEC configurations according to varying wave conditions. This approach aims to maximize energy capture and ensure sustainable array performance while addressing technical challenges. Simulation results indicate that the optimized configurations not only enhance energy extraction but also significantly reduce hydrodynamic loads, promising improved longevity and efficiency for multi-body WEC systems in extreme marine environments.

Two-stage bidirectional coevolutionary algorithm for constrained multi-objective optimization

Objective optimization and constraint satisfaction are two primary and conflicting tasks in solving constrained multi-objective optimization problems (CMOPs). To better trade off them, this paper proposes a two-stage bidirectional coevolutionary algorithm, termed C-TBCEA, for constrained multi-objective optimization. It consists of two stages, with each concentrating on specific targets, i.e., the first stage primarily focuses on objective optimization while the second stage focuses on constraint satisfaction by employing different evolutionary strategies at each stage. Via the synergy of the two stages, a dynamic trade-off between objective optimization and constraint satisfaction can be achieved, thus overcoming the distinctive challenges that may be encountered at different stages of evolution. In addition, to take advantage of both feasible and infeasible solutions, we employ two populations, i.e., the main population that stores the non-dominated feasible solutions and the archive population that maintains the informative infeasible solutions, to prompt the bidirectional coevolution of them. To validate the effectiveness of the proposed C-TBCEA, experiments are carried out on 6 CMOP test suites and 17 real-world CMOPs. The results demonstrate that the proposed algorithm is very competitive with 9 state-of-the-art constrained multi-objective optimization evolutionary algorithms (CMOEAs).

Reliable and cost-efficient session provisioning in CRNs using spectrum sensing as a service

With the advancement of wireless communication technologies, and the growing number of wireless and IoT applications that demand various types and volumes of data, Sensing as a Service (SaaS) has emerged as a necessary enabling business model for many of those applications. Spectrum Sensing as a Service (SSaaS) has also emerged as a form of SaaS that is concerned with the monitoring of wireless spectrum to facilitate its safe reuse by cognitive radio-enabled wireless users. SSaaS was primarily motivated by the need for a low-cost, accurate, and reliable spectrum sensing service to support a plethora of heterogeneous wireless devices and applications. Under the SSaaS model, clients need to pay the service provider for the sensing service they receive. In this paper, we address the problem of allocating spectrum channels to links of a given communication session in a cognitive radio network (CRN) that utilizes SSaaS. The objective is to allocate channels such that the worst link availability among the session is maximized and the spectrum access cost is minimized. A number of multi-objective evolutionary optimization algorithms (MOEAs) were used to solve this multi-objective optimization problem. Extensive experimentation was conducted to compare between these algorithms and identify the best ones to use. We also propose a post-processing greedy algorithm to further enhance the solution obtained by a MOEA algorithm. Results show that an improvement of up to 20% can be achieved using the proposed greedy algorithm under some network settings.

Interactive multiobjective evolutionary optimization model for dam management support

Dam management optimization is a complex multiobjective problem, whose current solutions struggle to spread in normal practice. Here, an interactive evolutionary multiobjective optimization method is proposed, which involves stakeholders by using multi-criteria decision analysis to embed a parsimonious elicitation of their preferences and expertise in the optimization process. Specifically: (i) one or more decision-makers are asked to rank a set of representative dam management strategies in order of preference; (ii) the ranking is used to build a preference model; and (iii) the preference model is used in an evolutionary multiobjective optimization algorithm to converge to the part of the Pareto front most preferred by the decision-makers. The inclusion of a user-friendly interaction in the optimization methodology makes the process more understandable for stakeholders and policy-makers, and it allows to address some of the key obstacles to the practical implementation of multiobjective dam management optimization, even when a large number of objectives is considered. The proposed methodology is applied to a case study (Lake Pozzillo, Sicily, Italy) with five objectives, where flood control is in contrast with irrigation water demand and hydropower production. Five simple and easy to implement optimal strategies are obtained by interacting with five decision-makers. The results are compared to the management practice currently used, indicating that the proposed approach can satisfy water demands while greatly improving flood attenuation.

Constrained multi-objective optimization assisted by convergence and diversity auxiliary tasks

In the field of constrained multi-objective optimization, constructing auxiliary tasks can guide the algorithm to achieve efficient search. Different forms of auxiliary tasks have their own advantages, and a reasonable combination can effectively improve the performance of the algorithm. Inspired by this, a Constrained Multi-objective Optimization Evolutionary Algorithm based on Convergence and Diversity auxiliary Tasks (CMOEA-CDT) is proposed. This algorithm achieves efficient search through simultaneous optimization and knowledge transfer of the main task, convergence auxiliary task, and diversity auxiliary task. Specifically, the main task is to find feasible Pareto front, which improves the global exploration and local exploitation of the algorithm through knowledge transfer from the convergence and diversity auxiliary tasks. In addition, the convergence auxiliary task helps the main task population traverse infeasible obstacles by ignoring constraints to achieve global search. The diversity auxiliary task aims to provide local diversity to the regions around the main task population to exploit promising search regions. The convergence and diversity of the algorithm are significantly improved by knowledge transfer between the convergence auxiliary task, diversity auxiliary task, and main task. CMOEA-CDT is compared with five state-of-the-art constrained multi-objective evolutionary optimization algorithms on 37 benchmark problems and a disc brake engineering design problem. The experimental results indicate that the proposed CMOEA-CDT respectively obtains 19 and 20 best results on the two indicators, and achieves the best performance on disc brake engineering design problem.

Machine Learning-Accelerated Multi-Objective Design of Fractured Geothermal Systems

Multi-objective optimization has burgeoned as a potent methodology for informed decision-making in enhanced geothermal systems, aiming to concurrently maximize economic yield, ensure enduring geothermal energy provision, and curtail carbon emissions. However, addressing a multitude of design parameters inherent in computationally intensive physics-driven simulations constitutes a formidable impediment for geothermal design optimization, as well as across a broad range of scientific and engineering domains. Here we report an Active Learning enhanced Evolutionary Multi-objective Optimization algorithm, integrated with hydrothermal simulations in fractured media, to enable efficient optimization of fractured geothermal systems using few model evaluations. We introduce probabilistic neural network as classifier to learns to predict the Pareto dominance relationship between candidate samples and reference samples, thereby facilitating the identification of promising but uncertain offspring solutions. We then use active learning strategy to conduct hypervolume based attention subspace search with surrogate model by iteratively infilling informative samples within local promising parameter subspace. We demonstrate its effectiveness by conducting extensive experimental tests of the integrated framework, including multi-objective benchmark functions, a fractured geothermal model and a large-scale enhanced geothermal system. Results demonstrate that the ALEMO approach achieves a remarkable reduction in required simulations, with a speed-up of 1-2 orders of magnitude (10-100 times faster) than traditional evolutionary methods, thereby enabling accelerated decision-making. Our method is poised to advance the state-of-the-art of renewable geothermal energy system and enable widespread application to accelerate the discovery of optimal designs for complex systems.

Exploring Evolutionary Algorithms for Multi-Objective Optimization in Seismic Structural Design

The seismic design of structures is an emerging practice in earthquake-resistant construction. Therefore, using energy-dissipation devices and optimizing these devices for various purposes are important. Evolutionary computation, nature-inspired, and meta-heuristic algorithms have been studied more in recent years for the optimization of these devices. In this study, the development of evolutionary algorithms for seismic design in the context of multi-objective optimization is examined through bibliometric analysis. In particular, evolutionary algorithms such as genetic algorithms and particle swarm optimization are used to optimize the performance of structures to meet seismic loads. While genetic algorithms are used to improve both the cost and seismic performance of the structure, particle swarm optimization is used to optimize the vibration and displacement performance of structures. In this study, a bibliometric analysis of 661 publications is performed on the Web of Science and Scopus databases and on how the research in this field has developed since 1986. The R-studio program with the biblioshiny package is used for the analyses. The increase in studies on the optimization of energy dissipation devices in recent years reveals the effectiveness of evolutionary algorithms in this field.

A clustering and vector angle-based adaptive evolutionary algorithm for multi-objective optimization with irregular Pareto fronts

A clustering and vector angle-based adaptive evolutionary algorithm for multi-objective optimization with irregular Pareto fronts

Dual-space distribution metric-based evolutionary algorithm for multimodal multi-objective optimization

Multimodal multi-objective optimization problems (MMOPs) are characterized by having multiple global or local Pareto solution sets. In most of multimodal multi-objective evolutionary algorithms, the distribution of population in objective space and decision space cannot be taken into account, simultaneously. In this paper, an innovative evolutionary algorithm based on dual-space distribution metric for multimodal multi-objective optimization (DsDMEA) is designed to solve this problem. Specifically, the two enhanced performance metrics are coupled together as a single dual-space distribution metric (DsDM) serving as the criterion for environmental selection. DsDMEA explores solutions on Pareto front and Pareto solutions sets that are not associated with the reference point, which show limited contributions to the distribution in dual spaces. Then DsDMEA removes any bi-noncontributing solutions using DsDM during environmental selection until the population size is met. This achieves a balance between the distribution of solutions and convergence in decision space. At the same time, this paper introduces a novel fitness computation method, enabling the precise localization of local Pareto fronts within the population. Finally, eight state-of-the-art multimodal multi-objective evolutionary algorithms are adopted to make a comparison on two types of benchmark to verify the performance of the DsDMEA. The experimental results demonstrated the DsDMEA effectiveness in solving MMOPs with local problems and traditional MMOPs.

A dynamic multi-objective optimization evolutionary algorithm based on classification of environmental change intensity and collaborative prediction strategy

A dynamic multi-objective optimization evolutionary algorithm based on classification of environmental change intensity and collaborative prediction strategy