Search

This study introduces a novel multi-objective cooperation search algorithm based on decomposition (MOCSA/D) to address multi-objective competitive challenges in engineering problem. Inspired by the optimization strategy of single-objective Cooperation Search Algorithm (CSA) and the decomposition framework of MOEA/D, MOCSA/D algorithm randomly generates initial solutions in the optimization space, and then repeatedly executes four search strategies until the end of iteration: Cooperative updating strategy gathers high-quality information to update solutions with balanced distribution. Reflective adjustment strategy expands the exploration range of the population, enabling the acquisition of solutions with strong optimization capabilities. Internal competition strategy selects superior individuals with better performance for subsequent optimization. Density updating strategy improves the competitiveness of optimized individuals within the population, fostering a more diverse solution set. Three numerical experiments (including DTLZ, WFG unconstrained test problems, ZXH_CF constrained test problems and RWMOP real-world multi-objective optimization problems) are tested to further comprehensively evaluate the dominant performance of MOCSA/D. The test results in different problem scenarios show that compared with the existing excellent evolutionary algorithms, MOCSA/D can always obtain a better, stable and uniform distribution of non-dominated solutions, and has higher solving efficiency and optimization quality under different performance evaluation metrics with the increasing difficulty of solving problems. Finally, the proposed algorithm is applied to the multi-objective reservoir engineering optimization problem to verify the feasibility of the decision scheme and the comprehensive benefit optimization of MOCSA/D. Overall, MOCSA/D can simplify the problem optimization difficulty based on decomposition mechanism, and improve the global optimization of population, path diversity and individual competition through different search strategies, which provides an advantageous tool for addressing multi-objective competitive challenges. • MOCSA/D with multiple adaptive strategies solves multi-objective optimization problems. • The effectiveness and reliability of MOCSA/D is proved through various test problems. • MOCSA/D demonstrates excellent performance in the application of reservoir operation. [ABSTRACT FROM AUTHOR]

To efficiently tackle the optimal operation problem of multi-stakeholder integrated energy systems (IESs), this paper develops a multi-objective optimization and multi-attribute decision-making support method. Mathematically, The optimal operation of IESs interconnected with distributed district heating and cooling units (DHCs) via the power grid and gas network, can be formulated as a multi-objective optimization problem considering both economic, reliability and environment-friendly objectives with numerous constraints of each energy stakeholder. Firstly, a multi-objective group search optimizer with probabilistic operator and chaotic local search (MPGSO) is proposed to balance global and local optimality during the random search iteration. The MPGSO utilizes a crowding probabilistic operator to select producers to explore areas with higher potential but less crowding and reduce the number of fitness function calculations. Moreover, a new parameter selection strategy based on chaotic sequences with limited computational complexity is adopted to escape the local optimal solutions. Consequently, a set of superior Pareto-optimal fronts could be obtained by the MPGSO. Subsequently, a multi-attribute decision-making support method based on the interval evidential reasoning (IER) approach is used to determine a final optimal solution from the Pareto-optimal solutions, taking multiple attributes of each stakeholder into consideration. To verify the effectiveness of the MPGSO, the DTLZ suite of benchmark problems are tested compared with the original GSOMP, NSGA-II and SPEA2. Additionally, simulation studies are conducted on a modified IEEE 30-bus system connected with distributed DHCs and a 15-node gas network to verify the proposed approach. The quality of the obtained Pareto-optimal solutions is assessed using a set of criteria, including hypervolume (HV), generational distance (GD), and Spacing index, among others. Simulation results show that the number of Pareto-optimal solutions (NPS) of MPGSO are higher by about 32.6 %-62.1%, computation time (CT) are lower by about 2.94 %-46.1 % compared with other algorithms. Besides, to further evaluate the performance of the proposed approach in addressing larger-scale issues, the study employs the modified IEEE 118-bus system of greater magnitude. The proposed MPGSO algorithm effectively handles multi-objective and non-convex optimization problems with Pareto sets in terms of better convergence and distributivity. • Proposing a decision-making model for the IES with multiple energy stakeholders. • Proposing a multi-objective probabilistic group search optimizer (MPGSO). • The performance of MPGSO is superior in terms of convergence speed and efficiency. • The IER contributes to overcome the uncertainties of decision pragmatically. [ABSTRACT FROM AUTHOR]

The vast search space in large-scale multi-objective optimization represents a significant challenge for evolutionary algorithms to converge towards the Pareto Front. As an effective search strategy, direction-guided sampling technique could improve the search efficiency by exploring along the approximated directions to approach the Pareto set. However, the approximated directions may fail to interact with the true Pareto set and result in inefficient search. To address this issue, a dual-sampling method is proposed in this paper. In addition to the samples along the directions approximated by direction-guided sampling, fuzzy Gaussian sampling is applied to adjust the search direction and generate more accurate and evenly distributed solutions. Moreover, a convergence-and-diversity-based mating selection is introduced to balance the exploration and exploitation. The experiments on 72 test benchmarks with bi- and tri-objectives and 500–5000 decision variables show the superiority of the proposed algorithm compare with the state-of-the-art algorithms. • A dual-sampling method is proposed including direction-guided sampling and fuzzy Gaussian sampling. • A convergence-and-diversity-based mating selection is introduced to balance the exploration and exploitation. • Experiments on 9 scalable LSMOPs with up to 50000 dimensions are implemented. [ABSTRACT FROM AUTHOR]

Achieving a balance between convergence and diversity is crucial in addressing multi-objective optimization problems (MOPs). In this paper, a multi-objective cat swarm optimization based on a new two-archive mechanism (MOCSO_TA) is proposed for the above challenge. In this approach, solutions that can promote convergence are stored by the convergence archive (CA). While solutions that can enhance diversity in the population are saved in the diversity archive (DA). Path planning is a critical process for unmanned aerial vehicles (UAVs), involving the identification of a route that is short and secure. Multi-objective algorithms have become a crucial approach for addressing UAV path planning problem, thus motivating the use of the proposed MOCSO_TA in path planning problem. The proposed algorithm is compared with two sets of representative multi-objective algorithms on the DTLZ, WFG, and ZDT benchmark problems. The experimental results demonstrate the outstanding performance of MOCSO_TA. The effectiveness of the MOCSO_TA is demonstrated by designing two terrains and comparing it with various multi-objective algorithms. The results confirmed the superiority of MOCSO_TA. • The algorithm we proposed can balance convergence and diversity in solving MOPs. • A two-archive mechanism with two update modes is applied to multi-objective cat swarm algorithm. • The multi-objective cat swarm algorithm has a seeking mode and a tracing mode. • The proposed algorithm is also applied to unmanned aerial vehicle (UAV) path planning problems in this paper. [ABSTRACT FROM AUTHOR]

Feature selection plays an essential role in data preprocessing, which can extract valuable information from extensive data, thereby enhancing the performance of machine learning classification. However, existing feature selection methods primarily focus on selecting feature subsets without considering the impact of classifier parameters on the optimal subset. Different from these works, this paper considers jointly optimizing the feature subset and classifier parameters to minimize the number of features and achieve a low classification error rate. Since feature selection is an optimization problem with binary solution space while classifier parameters involve both continuous and discrete variables, our formulated problem becomes a complex multi-objective mixed-variable problem. To address this challenge, we consider a single-objective optimization method and a multi-objective optimization approach. Specifically, in the single-objective optimization method, we adopt the linear weight method to convert our multiple objectives into a fitness function and then propose a mixed-variable grey wolf optimizer (MGWO) to optimize the function. The proposed MGWO introduces Chaos-Faure initialization, Log convergence factor adjustment, and optimal solution adaptive update operators to enhance its adaptability and balance the global and local search of the algorithm. Subsequently, an improved multi-objective grey wolf optimizer (IMOGWO) is introduced to directly address the problem. The proposed IMOGWO introduces improved initialization, local search, and binary variable mutation operators to balance its exploration and exploitation abilities, making it more suitable for our mixed-variable problem. Extensive simulation results show that our MGWO and IMOGWO outperform recent and classic baselines. Moreover, we also find that jointly optimizing classifier parameters can significantly improve classification accuracy. • Propose a joint feature selection and classifier parameter tuning method. • Formulate a complex mixed-variable optimization problem. • Introduce a single-objective optimization method called MGWO. • Present a multi-objective optimization approach, namely IMOGWO. • Perform extensive simulations to validate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Solving dynamic multi-objective optimization problems with time-varying Pareto front (PF) or Pareto set (PS) is a challenging task. Such problems require algorithms to react to environmental changes and efficiently track optimal solutions. For this purpose, a dynamic multi-objective sparrow search algorithm (SSA) with fusion prediction strategy, based on difference model and kernel extreme learning machine (DMOSSA-FPS), is proposed. Given the diversity of change characteristics, a single prediction model is insufficient. Therefore, based on the historical information of the population, a difference model and a kernel extreme learning machine are integrated for PS prediction. The former is used to predict the solutions of some individuals under approximate linear changes and the latter is employed for nonlinear predictions. In a new environment, the combined predictions increase the diversity of the initial population. Additionally, a new static optimizer is proposed, which combines decomposition- and dominance-based approaches to constitute a new individual screening mechanism. Then the optimization mode of SSA is introduced to enhance both algorithmic diversity and convergence rate. The experimental results on the DF test suite demonstrate that, compared with several other advanced algorithms, DMOSSA-FPS exhibits stronger convergence and robustness. • A novel method to solve dynamic multi-objective problems is proposed (DMOSSA-FPS). • Designing a fusion prediction strategy to complete the dynamic response. • Dominance degree sorting (DDS) is introduced to speed up the sorting. • A new static optimizer based on dominance and decomposition is proposed (MOSSA/DD). [ABSTRACT FROM AUTHOR]

The characteristic of multi-modal multi-objective optimization problems (MMOPs) is that multiple equivalent Pareto solution sets (PSs) in the decision space correspond to the same Pareto front (PF) in the objective space. The difficulty in solving the MMOPs lies in how to maintain the distribution in space. Many multi-modal multi-objective evolutionary algorithms (MMEAs) take convergence as the primary selection criterion, which makes it difficult for the algorithm to find all PSs in the decision space. In view of this situation, this paper proposes a partitioned knowledge-guided MMEA with multi-stage. The algorithm makes stage changes according to the proportion of evaluation consumed by the algorithm during the evolution, and adjusts the environment selection strategy as the stage changes. At the beginning of evolution, region division is carried out to prevent the solutions on each PS from interfering with each other and evolving independently. When the evaluation consumption reaches a certain proportion, it enters the middle stage. The information of the obtained solutions are used to guide the evolutionary direction of the population, and the deleted promising solutions are reclaimed. In the later stage, the steady state updating is performed to improve the distribution of population. The experimental results on four multi-modal multi-objective test suites with different features show that the proposed algorithm is more competitive than other seven excellent algorithms. • The regional division strategy makes the population move closer to potential PSs. • The solution reclaim strategy reclaims deleted promising solutions. • The environment selection makes stage changes by proportion of evaluation consumed. [ABSTRACT FROM AUTHOR]

The container loading problem (CLP) holds significant importance in logistics and supply chain management due to its direct influence on transportation costs and times, thereby impacting the competitive advantages of enterprises across industries. Although there are existing studies for CLP, there remains a gap in addressing the optimization of multiple objectives while satisfying practical constraints. Moreover, container loading methods must also meet performance requirements such as high computational speed, explainability, and implementation capability. Although meta-heuristic-based algorithms have shown effective computational capabilities and performance, such algorithms often being trapped in the local optima especially when searching in the vast solution space of the CLP, particularly as the quantity and diversity of cargo sizes and shapes increase. Motivated by realistic needs, this study aims to develop a UNISON-based framework that integrates the merging spaces algorithm, a novel simple but effective hybrid simplified swarm optimization genetic algorithm (SSO-GA), and a multi-populations co-evolution strategy to determine the loading sequence of parcels to maximize space utilization and weight balance while satisfying to practical constraints. Specifically, the merging spaces algorithm merges fragmented small spaces resulting from loaded parcels into larger spaces, thereby facilitating the accommodation of additional parcels. The hybrid SSO-GA splits encoded solutions and updates segment using novel strategies resulting in the rearrangement of a group of parcels and their corresponding layouts for better space utilization. Furthermore, the multi-populations co-evolution strategy enhances the diversity of the search space and stabilizes solution quality by simultaneously exploiting the best solutions and exploring alternative solutions during the solution update process. An empirical study was conducted by using a public benchmark dataset which demonstrated the practical effectiveness of the proposed framework by significantly improving average space utilization while ensuring center balance and satisfying practical constraints. Furthermore, this study can also serve as a digital support system capable of assisting decision-makers in optimizing container loading operations, thereby improving productivity and saving valuable time. [Display omitted] • Digital solution for optimizing container loading is developed. • Multiple objectives and constrains are considered for container loading optimization. • Multi-objectives Multi-populations Simplified swarm optimization (MOMP-SSO) is developed. • This solution integrated data preparing, meta-heuristics, and domain knowledge. • Container loading can be effectively optimized to improve space utilization and safety. [ABSTRACT FROM AUTHOR]

Nutcracker Optimization Algorithm (NOA) is a recently proposed meta-heuristic algorithm inspired by foraging and storing behavior of nutcracker birds. NOA demonstrates strong performance across various test sets and optimization problems. However, it faces challenges in effectively balancing exploration and exploitation, particularly in high-dimensional and complex applications. In this paper, an improved variant of NOA based on Bernoulli map strategy and seasonal behavior strategy, called INOA, is proposed. Firstly, the Bernoulli map strategy enhances the quality of the initial population during the initialization process. Secondly, the seasonal behavior strategy is employed to balance the exploration and exploitation of NOA, enabling it to effectively handle high-dimensional problems by improving convergence and exploration capabilities. Additionally, this paper extends INOA to a multi-objective version called MONOA, enabling the algorithm to solve multi-objective problems. The proposed algorithm, INOA, undergoes evaluation using 30 classical benchmark problems, CEC-2014, CEC-2017, CEC-2019 test suites, and two real-world engineering design problems. INOA's performance is compared with three categories of optimization methods: (1) recently-developed algorithms, i.e., NOA, BWO, DBO, RIME, MGO, HBA, and SO, (2) highly-cited algorithms, i.e., SMA, MPA, GWO, and (3) high-performing optimizers and winners of CEC competition, i.e., CJADE, L-SHADE-RSP, L-SHADE, and EBOwithCMAR. The proposed algorithm, MONOA, undergoes evaluation using well-known ZDT and DTLZ suites, as well as six constrained and engineering design problems. MONOA's performance is compared with some state-of-the-art approaches such as MOPSO, NSSO, MOGOA, MOSMA, and MOMGA. Five performance indicators are employed for comparison purposes. Experimental results and comparisons affirm the efficacy of INOA in solving complex and higher-dimensional optimization problems. Similarly, the findings underscore the effectiveness of MONOA in solving diverse multi-objective problems with distinct characteristics. • INOA: Efficient meta-heuristic inspired by nutcracker birds, excels in optimization. • INOA: Enhanced version balances exploration and exploitation. • INOA: Improves convergence, handles high-dimensional problems. • MONOA: Extended to address multi-objective optimization. • Rigorous testing validates superior performance of INOA and MONOA. [ABSTRACT FROM AUTHOR]

Traditional flexible job shop scheduling problems (FJSP) are mostly limited to deterministic environments. In actual production, some uncertain factors lead to changes in job processing time. When solving multi-objective fuzzy flexible job shop scheduling problems (MOFFJSP), the existing evolutionary algorithms do not fully utilize information transfer between levels during the population evolution process. Therefore, a multi-level guided evolutionary (MLGE) optimization method is proposed to solve MOFFJSP. In the proposed MLGE method, decomposition and dominance techniques are combined to balance the diversity and convergence performance of the algorithm. Meanwhile, the idea of the Jaya algorithm approaching good individuals and distancing poor individuals is introduced. Combined with improved Jaya operator operations, it is used to guide individual evolution, preserving and inheriting solutions that perform well in terms of convergence and diversity. Finally, numerous experiments are carried out to evaluate the effectiveness of MLGE. The results show that MLGE can provide promising results for MOFFJSP. Additionally, it shows how MLGE outperforms other comparison algorithms in terms of convergence and variety of solutions. • An initialization method combining global and local search is proposed. • A hybrid strategy for balancing algorithm diversity and convergence is designed. • A multi-level optimization strategy is designed to guide population evolution. • Developed a multi-neighborhood search technique to improve population quality. [ABSTRACT FROM AUTHOR]

Due to the widespread existence of constrained multi-objective optimization problems (CMOPs) in real life, many researchers start to research the constrained multi-objective evolutionary algorithms (CMOEAs). Therefore, a variety of CMOEAs have emerged. However, some of them still suffer from great difficulties when coping with CMOPs with complex feasible regions. To solve the issue, this article puts forward a two-stage evolutionary algorithm assisted by multi-archives for constrained multi-objective optimization, called MA-TSEA. In MA-TSEA, the evolution process is divided into two stages. In Stage 1, non-dominated solutions obtained by non-dominated sorting based on (M + 1) objectives (i.e., M objectives and the constraint violation degree (C V)) are stored in a temporary archive and merged with the parent population to improve the exploration ability of population. Then, MA-TSEA drives the populations to evolve from diverse directions by a multi-objective evolutionary algorithm, while the feasible solutions are saved in a formal archive. Next, the formal archive is updated to improve convergence and diversity. In Stage 2, the main population and archive population cooperate to evolve towards the constrained Pareto front (CPF), where the formal archive and population of Stage 1 are respectively used as the main population and archive population. The experimental studies on five benchmark test suites and five real-world applications demonstrate the superiority of MA-TSEA over the other seven state-of-art CMOEAs. • A MA-TSEA method is develop for CMOPs. • Two stages are used in MA-TSEA with different purposes. • Multiple archives assist the MA-TSEA find the complete constraint Pareto front. • MA-TSEA can achieve better results compared with its seven competitors. [ABSTRACT FROM AUTHOR]

This paper proposes an adaptive uniform search framework designed for constrained multi-objective optimization. The framework comprises three key components: a global uniform exploration strategy, a local greedy exploitation strategy, and a search switch mechanism. These components work together to facilitate comprehensive exploration of promising areas while maintaining a balance between global exploration and local exploitation. Specifically, the global uniform exploration strategy ensures even distribution within promising areas, preventing any oversights during exploration. The local greedy exploitation strategy divides these areas into sub-areas and employs a feasibility-led constraint handling technique to enhance efficiency in identifying optimal solutions. Additionally, the search switch dynamically adjusts the search strategy between global exploration and local exploitation. Numerical simulations on various benchmark suites and real-world problem demonstrate the strong performance of the framework in addressing constrained multi-objective optimization problems. The comparison results show that compared with eight recently proposed algorithms, the proposed framework is more robust in solving diverse constrained multi-objective optimization problems. • GUE removes close individuals, promoting even distribution. • LGE divides areas for effective local optimization. • A novel switch adapts search between GUE and LGE. [ABSTRACT FROM AUTHOR]

The cement grinding process requires monitoring of unit power consumption and specific surface area indicators to improve production efficiency and product quality. The strong coupling between operational variables necessitates careful adjustment of these variables to prevent production from becoming instability. Furthermore, the continuous and time-dependent nature of the process makes this adjustment particularly challenging. To address this dual dynamic problem, we have developed a multi-objective optimization model, with global optimization as the desired outcome, to optimize unit power consumption and specific surface area. Our optimization algorithm, termed "Optimization Algorithm - Dynamic Search Space and Rolling Time Domain" (OA-DSTR), considers the problem of dynamic production process. First, the algorithm uses a dynamic search space strategy, allowing for changes in the constraint range of the operational variables and introducing a fluctuation coefficient (FC) to measure solution rationality. Second, to deal with the time-dependent problem, we have added a rolling time domain strategy, enabling real-time monitoring of the cement grinding process. The experimental results show that OA-DSTR not only ensures global optimization, but also realizes the tracking of dynamic conditions, improving the stability of the cement grinding process and achieving a lower FC value. [Display omitted] • A prediction model of energy consumption in cement calcination system is proposed. • An improved prediction algorithm is proposed to predict f-cao. • The multi-objective optimization model of cement calcination system is designed. • A multi-objective optimization algorithm considering dual dynamic problems is proposed. • Achieved the goal of energy saving, quality improvement and system stability. [ABSTRACT FROM AUTHOR]

Numerous multi-objective evolutionary algorithms have recently been proposed for the leader selection or archive maintenance process via using reference sets. Despite the potential of this strategy has been demonstrated in the existing literatures, a significant drawback is that this methodology requires additional parameters or predefined reference points, which leads to increasing the complexity in real-world applications and reducing the versatility in addressing various optimization challenges. Novel to this study, a new convergence contribution (CC) evaluator without extra parameters and predefined reference points is presented for convergence evaluation, where the inspiration is drawn from the concept of an ideal point on a Pareto front and the divide-and-conquer technique. Specifically, the local ideal points are introduced in this paper by dynamically fabricating from the approximate Pareto front. Furthermore, the CC evaluator and parallel cell distance (PCD) are cooperatively integrated into a multi-objective particle swarm optimization (MOPSO/CP) to enhance both the global best solution selection and archive maintenance strategies. Comparative experiments on 21 benchmark test functions exhibited that the performances in terms of inverted generational distance and hypervolume of the proposed MOPSO/CP was the best among those of the chosen competitive algorithms. The significant role of the cooperative mechanism and the CC evaluator is further verified by ablation studies. The superiority of the designed algorithm against its competitors is unequivocally highlighted by the experimental results. • A new evaluator is proposed to better measure the convergence contribution for an evolutionary algorithm. • A new local ideal point set strategy is introduced to calculate the new evaluator. • The CC and PCD are cooperatively integrated into a MOPSO as MOPSO/CP. [ABSTRACT FROM AUTHOR]

Striking a balance between diverse constraints and conflicting objectives is one of the most crucial issues in solving constrained multi-objective optimization problems (CMOPs). However, it remains challenging to existing methods, due to the reduced search space caused by the constraints. For this issue, this paper proposes a Pareto fronts relationship identification-based two-stage constrained evolutionary algorithm called RITEA, which balances objective optimization and constraint satisfaction by identifying and utilizing the relationship between the unconstrained Pareto front (UPF) and the constrained Pareto front (CPF). Specifically, the evolutionary process is divided into two collaborative stages: training stage and reinforcement stage. In the training stage, a relationship identification method is developed to estimate the relationship between UPF and CPF, which guides the population search direction. In the reinforcement stage, the corresponding evolutionary strategies are designed based on the identified relationship to enhance the accurate search on the CPF. Furthermore, a dynamic preference fitness function (termed DPF) is designed to adaptively maintain the balance of search preference between convergence and diversity. Compared to seven state-of-the-art algorithms on 36 benchmark CMOPs in three popular test suites, RITEA obtains 77.8% of the best IGD values and 66.7% of the best HV values. The experimental results show that RITEA exhibits highly competitively when dealing with CMOPs. [Display omitted] • RITEA, a two-stage constrained evolutionary algorithm for CMOPs. • Identify and utilize the relationship between UPF and CPF to balance optimization and constraint satisfaction. • Utilizes collaborative training stage and reinforcement stage to guide population search accurately. • Introduction of a dynamic preference fitness function to adaptively balance the search preferences between convergence and diversity. [ABSTRACT FROM AUTHOR]

Normalization of objectives plays a crucial role in evolutionary multi-objective optimization (EMO) to handle objective functions with different scales, which can be found in real-world problems. Although the effect of normalization methods on the performance of EMO algorithms has been investigated in the literature, that of preference-based EMO (PBEMO) algorithms is poorly understood. Since PBEMO aims to approximate a region of interest, its population generally does not cover the Pareto front in the objective space. This property may make normalization of objectives in PBEMO difficult. This paper investigates the effectiveness of three normalization methods in three representative PBEMO algorithms. We present a bounded archive-based method for approximating the nadir point. First, we demonstrate that the normalization methods in PBEMO perform significantly worse than that in conventional EMO in terms of approximating the ideal point, nadir point, and range of the PF. Then, we show that PBEMO requires normalization of objectives on problems with differently scaled objectives. Our results show that there is no clear "best normalization method" in PBEMO, but an external archive-based method performs relatively well. • This paper investigates the effectiveness of three normalization methods in three representative PBEMO algorithms. • We demonstrate that the normalization methods in PBEMO perform significantly worse than that in conventional EMO in terms of approximating the ideal point, nadir point, and range of the PF. scaled objectives. • We show that there is no clear "best normalization method" in PBEMO, but an external archive-based method performs relatively well. [ABSTRACT FROM AUTHOR]

Spinning reserve based on solar radiation prediction can ensure the secure operation of large-scale grid-connected photovoltaic systems, but irrational spinning reserve can lead to substantial economic losses and even grid collapse. Therefore, it is imperative to identify characteristic patterns of solar radiation and establish an effective solar radiation prediction model. However, existing hybrid models often struggle to efficiently recognize and leverage input features, compromising the robustness of the model. To address this limitation, this study proposes an integrated skip-convolutional network with residual learning and feature extraction (InSCNet), which enhances the capability to represent information data and thereby improves the stability and accuracy of solar radiation prediction by employing a series of feature extraction and prediction blocks with residual learning. InSCNet includes a sophisticated feature extraction module to effectively address the underutilization of feature information, and it incorporates residual learning, skip-convolution, and recursive networks in the prediction module, reducing the risk of gradient vanishing or gradient explosion while enhancing prediction accuracy. Additionally, an interval estimation method with adaptively chosen distributions is introduced, which extends the prediction interval estimation method. The proposed InSCNet is rigorously evaluated using datasets from three major cities in Pakistan. Experimental results demonstrate that InSCNet outperforms existing solutions in both point and interval predictions. • A feature extraction module for solar radiation prediction is designed. • The prediction module is constructed using residual learning and skip-convolution. • An optimization layer of a multi-objective Kepler optimization algorithm is developed. • An interval estimation method with adaptively chosen distributions is introduced. [ABSTRACT FROM AUTHOR]

Dynamic multi-objective optimization problems (DMOPs) can be utilized to model certain real-world problems that have a dynamic nature. Algorithms for solving DMOPs can be evaluated and improved by comparing their performance on different benchmarks. However, some existing benchmarks for DMOPs have the limitation of non-uniform weights for decision variables. Additionally, dynamic many-objective optimization problems (DMaOPs) involve more than three objectives, but only a few existing benchmarks can be extended to accommodate DMaOPs. Furthermore, some existing performance measures for DMOPs may not effectively compare the relative performance differences between multiple algorithms or evaluate the search uniformity among different objectives. In this paper, we propose improvements to an existing benchmark for DMOPs by expanding the impact range of decision variables. Moreover, a benchmark framework that can be extended to accommodate DMaOPs is proposed, thus addressing a research gap between the optimization of DMOPs and DMaOPs. Additionally, a set of performance measures for DMOPs are proposed, which can evaluate the relative performance and search uniformity of multi-objective optimization algorithms. By comparing the performance of state-of-the-art and commonly used algorithms on test problems, we can gain a better understanding of the characteristics and strengths and weaknesses of the algorithms and test problems. • A set of improved DMOPs test problems are proposed in this paper. • A scalable DMaOP benchmark framework is proposed in this paper. • A set of performance measures that applies to both MOPs and DMOPs are proposed. [ABSTRACT FROM AUTHOR]

With the development of economic globalization and sustainable manufacturing, energy-aware scheduling of distributed manufacturing systems has become a research hot topic. However, energy-aware scheduling of distributed hybrid flow-shop is rarely explored. Thus, this paper is the first attempt to study an energy-aware distributed hybrid flow-shop scheduling problem (DHFSP). We formulate a novel mathematical model of the DHFSP with minimizing makespan and total energy consumption (TEC) criteria. A hybrid multi-objective iterated greedy (HMOIG) approach is proposed to address this energy-aware DHFSP. In this proposed HMOIG, firstly, a new energy-saving method is presented and introduced into the model for reducing TEC criterion. Secondly, an integration initialization scheme is devised to produce initial solutions with high quality. Thirdly, two properties of DHFSP are used to invent a knowledge-based local search operator. Finally, we validate the effectiveness of each improvement component of HMOIG and compare it with other well-known multi-objective evolutionary algorithms on instances and a real-world case. Experimental results manifest that HMOIG is a promising method to solve this energy-aware DHFSP. • A new mathematical model for sustainable DHFSP is formulated. • A hybrid multi-objective IG algorithm is designed to solve DHFSP. • An integration initialization is proposed to produce one high-quality population. • A new energy saving strategy is recommended to address this problem. [ABSTRACT FROM AUTHOR]

In dynamic multi-objective optimization problems (DMOPs), objective functions, problem parameters, and constraints may change over time. Mainly, DMOPs use response mechanisms to generate the initial population after the environment changes. In this research, we develop an adaptive version of the combinational response mechanism (ACRM). ACRM uses three response mechanisms based on diversity, prediction, and memory to form the initial population. In ACRM, the number of solutions generated by a response mechanism is determined by reinforcement learning according to the severity of environmental changes. The background knowledge is transferred to reinforcement learning using the Q-value initialization method. Thus, in the early stages of optimization, when the experience gained from the environment is low, the proposed algorithm improves its performance using background knowledge. Also, we develop a new combinational constraint handling technique (CCHT). This method uses the dynamic information of the environment (i.e. the ratio of feasible solutions) to choose the appropriate constraint handling technique. The results of the tests on 23 dynamic test functions and seven dynamic constrained test functions indicate that the performance of the proposed algorithm can compete with advanced evolutionary algorithms in terms of the degree of convergence and variety of solutions. Permanent link to reproducible Capsule: < https://doi.org/10.24433/CO.1949267.v1 >. • Developing an adaptive CRM that uses reinforcement learning. • Extracting valuable information from the environment to adapt to changes. • Using reinforcement learning to determine the number of solutions. • Proposing a new combinational constraint-handling technique (CCHT). • Generate six new DCMOPs by combining ACRM with six constraint-handling techniques. [ABSTRACT FROM AUTHOR]

In the field of community detection, node attribute information plays an important role in community division. Existing methods use topology structure and node attribute information to discover non-overlapping communities. However, so far, attribute information has not been fully utilized in overlapping community detection. To address this, we propose a new overlapping community detection method called "evolutionary multi-objective overlapping community detection based on Fusion of internal and external Connectivity and Correction of Node Intimacy" (FCCNI). Firstly, we propose a fusion strategy based on internal and external connectivity, which integrates some communities with sparse intra-connections and dense inter-connections. This automatically determines, reconfirms, and corrects the number of communities. Secondly, a function is designed to calculate the intimacy between nodes, and the node label with the highest intimacy is selected to correct the current wrong node. The correction strategy is used in two stages of initialization and multi-objective evolution to obtain a more accurate node label. Finally, a method that considers not only the connections of the community, but also the node attribute, is designed to obtain the overlapping community indirectly from the non-overlapping community. The experimental results on five real-life networks and four classical synthetic networks show that FCCNI achieves better overlapping community division, compared with six state-of-the-art comparison algorithms from the literature. • This paper proposes an attribute information-assisted overlapping community detection method called FCCNI. • The initial stage is supplemented by the fusion strategy based on internal and external connectivity, FCCNI designs a function to calculate the intimacy between nodes. • The correction strategy is used in two stages of initialization and multi-objective evolution to obtain a more accurate node label. • FCCNI exploits the internal connection of the communities and the node attribute to obtain the overlapping community indirectly from the non-overlapping community. [ABSTRACT FROM AUTHOR]

A central problem in network dynamics is understanding how influence spreads through a social network. This problem can be studied from an optimization approach. The aim is to find an initial seed of actors, with certain size restrictions, capable of maximizing or minimizing the activation of other actors in the network through a given influence spread model. The maximization and minimization versions of this problem have been extensively studied. In recent years, the min–max multi-objective version was defined, which involves finding the smallest seed capable of maximizing the influence spread in the network. Searching for exact solutions in these optimization problems is not feasible, even for relatively small networks. Hence, various approximation techniques have been proposed in recent years, with bio-inspired algorithms based on metaheuristics standing out among them. However, the max–min multi-objective version of the problem remains open. This article formally defines the max–min influence spread problem, aiming to find the maximum seed with the minimum spread capacity. We propose a strategy that uses solutions from the min–max version of the problem to reduce the search space, allowing us to avoid trivial solutions. The potential applications of this max–min version are diverse, e.g., finding clusters less susceptible to diseases in a contagion network or the most inefficient coalitions in a voting system. Using swarm intelligence metaheuristics methods as in the min–max version, the results obtained on real social networks show that this approach exhibits rapid convergence, reaching a seed encompassing 51.3% of the actors who could not influence others within the network. Similarly, for a more complex network, the approach is able to generate a seed where 71.8% of the actors showed no influence over others. • We define the max–min influence spread problem as multi-objective optimization. • We solve it using swarm intelligence-based methods on real social networks. • The PSO algorithm allows efficient solution sets with non trivial solutions. [ABSTRACT FROM AUTHOR]

Algorithms designed to solve dynamic multi-objective optimization problems (DMOPs) need to consider all of the multiple conflicting objectives to determine the optimal solutions. However, objective functions, constraints or parameters can change over time, which presents a considerable challenge. Algorithms should be able not only to identify the optimal solution but also to quickly detect and respond to any changes of environment. In order to enhance the capability of detection and response to environmental changes, we propose a dynamic multi-objective optimization (DMOO) algorithm based on the detection of environment change intensity and ensemble learning (DMOO-DECI&EL). First, we propose a method for detecting environmental change intensity, where the change intensity is quantified and used to design response strategies. Second, a series of response strategies under the framework of ensemble learning are given to handle complex environmental changes. Finally, a boundary learning method is introduced to enhance the diversity and uniformity of the solutions. Experimental results on 14 benchmark functions demonstrate that the proposed DMOO-DECI&EL algorithm achieves the best comprehensive performance across three evaluation criteria, which indicates that DMOO-DECI&EL has better robustness and convergence and can generate solutions with better diversity compared to five other state-of-the-art dynamic prediction strategies. In addition, the application of DMOO-DECI&EL to the real-world scenario, namely the economic power dispatch problem, shows that the proposed method can effectively handle real-world DMOPs. • A dynamic multi-objective optimization algorithm based on ensemble learning. • A detection method to probe the intensity of environmental change. • A method of boundary point to enhance the diversity and uniformity of the solution. • Five state-of-the-art algorithms to compare experimental results. [ABSTRACT FROM AUTHOR]

This manuscript presents an efficient multi-objective optimization method based on using particle swarm optimization together with a desirability function that can be applied where the response variables may have an opposite behavior and where the range of variation of the independent variables as well as those of the responses are subjected to constraints, which has a great deal of industrial interest. For example, maintaining roughness and dimensional tolerances within a tolerance range is determined by the design requirements of the manufactured parts (shape errors, microgeometry errors, etc.) and these requirements must be met in the manufacture of parts. It is demonstrated that it is possible to obtain optimal results in the ranges of variation considered for the independent variables, with regard to those obtained by experimentation. Similarly, models based on Adaptive Network-based Fuzzy Inference Systems are used to solve the problem that may arise from the inadequate fitting of the regression models. Thus, thanks to this present study a fast and efficient method is available for the multiple-optimization of response variables, subject to constraints on both response and independent variables, which are obtained from experiments and modelled by means of soft computing techniques. Furthermore, it is also demonstrated that it is possible to obtain technology tables for various manufacturing processes, which is of great interest from a technological point of view so as to obtain the most suitable processing conditions. • A novel method for multiple-objective optimization based on PSO is proposed. • The algorithm performs efficiently and with low computational cost. • Technology tables for various manufacturing processes can be obtained. • Optimal manufacturing parameters selection in EDM has been obtained. [ABSTRACT FROM AUTHOR]

In practice, the decision-maker (DM) may be only interested in a particular part of Pareto optimal front (PF). For this reason, many preference-based multi-objective evolutionary algorithms (MOEA) have been proposed to find a solution set that approximates the region of the interest (ROI). Most existing preference-based methods focus on selecting solutions in the ROI. Nevertheless, the enhancement of convergence in preference-based MOEAs has been neglected. Most decomposition-based approaches often employ the achievement scalarizing function (ASF) as their scalarizing function. However, it holds a weaker search ability than the weighted sum function (WSF) despite its capability to tackle problems with arbitrary PF geometries. In order to strengthen the selective pressure toward the PF, this paper proposes a new scalarizing function, LSF, which is a linear combination of the WSF and the ASF. Then, a simple adaptive penalty scheme is employed in LSF to balance the search ability and robustness. To focus the search on the ROI, we develop a reference point adjustment method that dynamically adjusts the position of the reference point according to the distance from the approximated target point. We apply the above two innovations to the MOEA/D framework and propose a new preference-based MOEA, namely RAMOEAD. Experimental results show that RAMOEAD is highly competitive when compared with five state-of-the-art preference-based MOEAs. Finally, the proposed algorithm is extended to double reference points for solving the problems in which the ROIs are defined by the reservation and aspiration points. [Display omitted] • A family of new scalarizing functions are proposed to improve the preference-based MOEAs. • A reference point adjustment method is proposed to control the size of ROI. • Furthermore, the proposed algorithm is extended to solve the problems in which the preference information is defined by aspiration and reservation points. [ABSTRACT FROM AUTHOR]

Radial basis function (RBF) models have attracted a lot of attention in assisting evolutionary algorithms for solving computationally expensive optimization problems. However, most RBFs cannot directly provide the uncertainty information of their predictions, making it difficult to adopt principled infill sampling criteria for model management. To overcome this limitation, an inverse distance weighting (IDW) and RBF based surrogate assisted evolutionary algorithm, named IR-SAEA, is proposed to address high-dimensional expensive multi-objective optimization problems. First, an RBF-IDW model is developed, which can provide both the predicted objective values and the uncertainty of the predictions. Moreover, a modified lower confidence bound infill criterion is proposed based on the RBF-IDW for the balance of exploration and exploitation. Extensive experiments have been conducted on widely used benchmark problems with up to 100 dimensions. The empirical results have validated that the proposed algorithm is able to achieve a competitive performance compared with state-of-the-art SAEAs. • Proposed an surrogate assisted evolutionary algorithm. • Builded surrogates based on the radial basis function and inverse distance weighting. • Proposed a modified lower confidence bound to balance exploration and exploitation. • Evaluated algorithm on three test suites with up to 100 dimensions. • Empirical results show effectiveness in solving high-dimensional expensive MOPs. [ABSTRACT FROM AUTHOR]

Multi-modal multi-objective optimization problems (MMOPs) refer to several solutions in the decision space that share the same or similar objective value. Balancing the diversity of the objective space and decision space while maintaining the convergence of the population is a challenging and important problem. To address this issue, a novel multi-modal multi-objective evolutionary algorithm (MMEA) named MMEA-SND is proposed in this study. In the MMEA-SND, to locate Pareto-optimal solutions, and improve the diversity of solutions in the decision space, a diversity fitness is designed by the niche method to calculate the fitness of solutions in the diversity archive. In order to balance the diversity of solutions in the objective space and decision space, a scaled niche distance (SND) method is proposed in environmental selection. In this context, SND are utilized to measure the distances between each solution in the objective space and decision space. Furthermore, a parameter is implemented to avoid disregarding locally optimal solutions. To verify the performance of MMEA-SND, six state-of-the-art MMEAs are adopted to make a comparison on 42 benchmark problems. The experimental results show that the proposed MMEA-SND achieves a competitive performance in solving MMOPs. • A niching-based strength Pareto dominated is proposed in the diversity fitness. Additionally, a dynamic niching-based mechanism is used to calculate the diversity of neighborhood solutions. • A scaled niche distance method is designed to balance the diversity in objective space and decision space. • A parameter is proposed to distinguish solutions between local and global PFs. [ABSTRACT FROM AUTHOR]

With the growing maturity of multi-robot system technology, its applications have expanded across various domains. This paper addresses the critical issue of task allocation in nuclear accident rescue scenario, which plays a pivotal role in the success of such operations. The problem is formulated as a multi-objective optimization problem, taking into account three key indicators: execution time, radiation accumulation, and waiting cost. To effectively tackle this problem, an two-stage evolutionary algorithm is proposed. Firstly, a solution encoding method and a crossover mutation method is devised tailored to the problem's characteristics. Secondly, a two-stage search strategy is designed. In the first stage, a fixed population size and shift-based density estimation method are used to quickly converge the solution set to the Pareto front. The latter stage uses an infinite size population to find as many Pareto solutions as possible. Finally, a local search strategy is introduced to improve the quality of solution set. In the experimental section, our proposed method is compared with five state-of-the-art algorithms on nine instances of varying scales. Across five evaluation metrics, the proposed algorithm demonstrates competitive performance on all instances. These results underscore the efficacy and competitiveness of our approach in tackling the task allocation problem in multi-robot systems within nuclear accident rescue. • A multi-robot task allocation problem in nuclear scenario is presented. • An evolutionary algorithm with two stages is proposed to solve the problem. • A local search mechanism combining crowding distance and 2-opt is proposed. • Nine test instances ensure the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Due to the dynamics and uncertainty of the dynamic multi-objective optimization problems (DMOPs), it is difficult for algorithms to find a satisfactory solution set before the next environmental change, especially for some complex environments. One reason may be that the information in the environmental static stage cannot be used well in the traditional framework. In this paper, a novel framework based on generational and environmental response strategies (FGERS) is proposed, in which response strategies are run both in the environmental change stage and the environmental static stage to obtain population evolution information of those both stages. Unlike in the traditional framework, response strategies are only run in the environmental change stage. For simplicity, the feed-forward center point strategy was chosen to be the response strategy in the novel dynamic framework (FGERS-CPS). FGERS-CPS is not only to predict change trend of the optimum solution set in the environmental change stage, but to predict the evolution trend of the population after several generations in the environmental static stage. Together with the feed-forward center point strategy, a simple memory strategy and adaptive diversity maintenance strategy were used to form the complete FGERS-CPS. On 13 DMOPs with various characteristics, FGERS-CPS was compared with four classical response strategies in the traditional framework. Experimental results show that FGERS-CPS is effective for DMOPs. • The novel framework includes the generational response strategy. • The feed-forward center point strategy was an example run in the novel framework. • The statistical results showed that the proposed strategy is very competitive. • The experiments denoted the novel framework is better. [ABSTRACT FROM AUTHOR]

This paper gives attention to multi-objective optimization in scenarios where objective function evaluation is expensive, that is, expensive multi-objective optimization. We firstly propose a cluster-based neighborhood regression model, which incorporates the linear regression technique to predict the descent direction and generate new potential offspring. Combining this model with the classical decomposition-based multi-objective optimization framework, we propose an efficient and effective algorithm for tackling computationally expensive multi-objective optimization problems. As opposed to the conventional approach of replacing the original time-consuming objective functions with the approximated ones obtained by surrogate model, the proposed algorithm incorporates the proposed regression model to serve as an operator producing higher-quality offspring so that the algorithm requires fewer iterations to reach a given solution quality. The proposed algorithm is compared with several state-of-the-art surrogate-assisted algorithms on a variety of well-known benchmark problems. Empirical results demonstrate that the proposed algorithm outperforms or is competitive with other peer algorithms, and has the ability to keep a good trade-off between solution quality and running time within a fairly small number of function evaluations. In particular, our proposed algorithm shows obvious superiority in terms of the computational time used for the algorithm components, and can obtain acceptable solutions for expensive problems with high efficiency. • This paper proposes a cluster-based neighborhood regression model. • This paper proposes an efficient and effective algorithm called MONRO. • The proposed model can predict descent direction and generate potential offspring. • The MONRO can allocate more computation resources to the more important subproblems. • The MONRO algorithm is of high efficiency when tackling expensive multi-objective optimization problems. [ABSTRACT FROM AUTHOR]

Most current evolutionary multi-objective optimization (EMO) algorithms perform well on multi-objective optimization problems without constraints, but they encounter difficulties in their ability for constrained multi-objective optimization problems (CMOPs) with low feasible ratio. To tackle this problem, this paper proposes a multi-objective differential evolutionary algorithm named MODE-SaE based on an improved epsilon constraint-handling method. Firstly, MODE-SaE self-adaptively adjusts the epsilon level in line with the maximum and minimum constraint violation values of infeasible individuals. It can prevent epsilon level setting from being unreasonable. Then, the feasible solutions are saved to the external archive and take part in the population evolution by a co-evolution strategy. Finally, MODE-SaE switches the global search and local search by self-switching parameters of search engine to balance the convergence and distribution. With the aim of evaluating the performance of MODE-SaE, a real-world problem with low feasible ratio in decision space and fourteen bench-mark test problems, are used to test MODE-SaE and five other state-of-the-art constrained multi-objective evolution algorithms. The experimental results fully demonstrate the superiority of MODE-SaE on all mentioned test problems, which indicates the effectiveness of the proposed algorithm for CMOPs which have low feasible ratio in search space. • To solve constrained multi-objective optimization problems with low feasible ratio. • An algorithm based on an improved epsilon constraint-handling method is proposed. • A co-evolution strategy of the external archive is used to save feasible solutions. • Our algorithm self-switches parameters of DE to balance convergence and distribution. • Our algorithm provides an effective tool to solve the above problems. [ABSTRACT FROM AUTHOR]

Cloud detection algorithms have emerged to automate image data analysis because of its prime influential factor in remote sensing image quality. Cloud detection algorithm still needs domain-expert intervention and large number of training examples to ensure good performance whose acquirement becomes difficult due to unavailability of labeled data as well as the time and process heads involved. The paper puts forward multi-objective social spider optimization (MOSSO) based efficient clustering technique to detect clouds in the visible range. This paper explains the proposed MOSSO algorithm along-with the analysis carried on 14 benchmark two-objective test problems against MOEA/D, MODE, MOPSO and SPEA2 multi-objective algorithms. Further, the strengths and weaknesses of the proposed algorithm are analyzed and have been used for the implementation of an efficient clustering technique named as MOSSO-C. Optimal centroid matrix for clustering is attained in MOSSO-C through environmental selection whose performance evaluation has been done on six synthetic databases and are compared with above mentioned conventional multi-objective algorithms. The obtained results encourage the use of MOSSO-C technique to get labeled data for training process of neural network classifier. This approach efficiently classifies the cloudy pixels against various Earth's surfaces (water, vegetation and land). The paper also discusses the performance evaluation of proposed technique on four Landsat 8 data which shows on an average 96.37% performance accuracy in detecting cloudy pixels. • Multi-objective social spider optimization (MOSSO) is proposed. • A Roulette wheel switching technique is proposed to obtain fittest striplings. • Cloud detection technique is formulated as a multi-objective clustering problem using MOSSO algorithm. • To select optimal centroid, an environmental selection method is proposed. • The optimal centroid act as target vector for a NN classifier to detect cloud. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a new meta-heuristic algorithm called Pathfinder Algorithm (PFA) to solve optimization problems with different structure. This method is inspired by collective movement of animal group and mimics the leadership hierarchy of swarms to find best food area or prey. The proposed method is tested on some optimization problems to show and confirm the performance on test beds. It can be observed on benchmark test functions that PFA is able to converge global optimum and avoid the local optima effectively. Also, PFA is designed for multi-objective problems (MOPFA). The results obtained show that it can approximate to true Pareto optimal solutions. The proposed PFA and MPFA are implemented to some design problems and a multi-objective engineering problem which is time consuming and computationally expensive. The results of final case study verify the superiority of the algorithms proposed in solving challenging real-world problems with unknown search spaces. Furthermore, the method provides very competitive solutions compared to well-known meta-heuristics in literature, such as particle swarm optimization, artificial bee colony, firefly and grey wolf optimizer. Highlights • A new heuristic algorithm has been proposed. • The method is a swarm-based algorithm and different in mathematical model. • The proposed method has been tested on some test beds. • The proposed method showed a superior performance to find global optima. • Also, it has been applied to a real engineering problem and found good results. [ABSTRACT FROM AUTHOR]

Abstract In this paper, we addressed two significant characteristics in practical casting production, namely tolerated time interval (TTI) and limited starting time interval (LimSTI). With the consideration of TTI and LimSTI, a multi-objective flexible job-shop scheduling model is constructed to minimize total overtime of TTI, total tardiness and maximum completion time. To solve this model, we present a hybrid discrete particle swarm optimization integrated with simulated annealing (HDPSO-SA) algorithm which is decomposed into global and local search phases. The global search engine based on discrete particle swarm optimization includes two enhancements: a new initialization method to improve the quality of initial population and a novel gBest selection approach based on extreme difference to speed up the convergence of algorithm. The local search engine is based on simulated annealing algorithm, where four neighborhood structures are designed under two different local search strategies to help the proposed algorithm jump over the trap of local optimal solution. Finally, computational results of a real-world case and simulation data expanded from benchmark problems indicate that our proposed algorithm is significant in terms of the quality of non-dominated solutions compared to other algorithms. Highlights • Introducing a novel FJSP model with the consideration of casting characteristics. • Presenting a new initialization method based on tolerated time interval. • Proposing a novel g B e s t selection method based on extreme difference. • Designing four neighborhood structures under two different local search strategies. • Confirming the performance of HDPSO-SA in a real-world case and simulation data. [ABSTRACT FROM AUTHOR]

Abstract Wellbore trajectory design is a determinant issue in drilling engineering. The criteria to evaluate a wellbore trajectory are summarized as the total trajectory length, the torque and the well profile energy in this paper. By minimizing the wellbore trajectory length, torque and profile energy simultaneously, it is most likely that a wellbore trajectory designed to arrive at the specific target can be drilled more safely, quickly and cheaply than other potential trajectories. However, these three objectives are often in conflict with each other and related in a highly nonlinear relationship. A multi-objective cellular particle swarm optimization (MOCPSO) with an adaptive neighborhood function is developed in this paper. Then, MOCPSO is applied with the three objective functions to gain a set of Pareto optimal solutions that are beneficial for a less risky and less costly wellbore trajectory design option. Besides, MOCPSO's performance is compared with multi-objective PSO, multi-objective evolutionary algorithm based on decomposition (MOEA/D) and non-dominated sorting genetic algorithm-II (NSGA-II). Effect of the proposed neighborhood function is also investigated by making contrasts with the commonly used four neighborhood templates. Moreover, the radius parameter in the adaptive neighborhood function is analyzed to reveal its influence on the optimization performance. It can be inferred that MOCPSO is statistically superior to both multi-objective PSO, NSGA-II and MOEA/D at the 0.05 level of significance on the wellbore trajectory design problem. And the proposed adaptive neighborhood function performs either comparable or better as compared to the other commonly used neighborhood functions. According to the parameter analysis, it can be concluded that the MOCPSO approach with radius value of 1or 1.5 has a better statistical performance. Highlights • Length, torque and profile energy are utilized to evaluate a wellbore trajectory. • MOCPSO with an adaptive neighborhood function is developed. • MOCPSO is compared with MOPSO, NSGA-II and MOEA/D. • Performance of adaptive neighborhood function is investigated. [ABSTRACT FROM AUTHOR]

Abstract Identifying a set of individuals that have an influential relevance and act as key players is a matter of interest in many real world situations, especially in those related to the Internet. Although several approaches have been proposed in order to identify key players sets, they mainly focus just on the optimization of a single objective. This may lead to a poor performance since the sets identified are not usually able to perform well in real life applications where more objectives of interest are taken into account. Multi-objective optimization seems the natural extension for this task, but there is a lack of this type of methodologies in the scientific literature. An efficient Multi-Objective Artificial Bee Colony (MOABC) algorithm is proposed to address the key players identification problem and is applied in the context of six networks of different dimensions and characteristics. The proposed approach is able to best identify the key players than the ones previously proposed, especially in the context of large social networks. The model performance of the proposed approach has been evaluated according to different quality metrics. The results from the MOABC execution show important improvements with respect to the best multi-objective results in the scientific literature, specifically, in average, 13.20% of improvement in Hypervolume, 120.39% in Coverage Relation and 125.52% in number of non-dominated solutions. Even more, the proposed algorithm is also more robust when repeating executions. Highlights • A multi-objective approach to identify key players sets in social networks is proposed. • This computer-based approach is tested in six networks of different characteristics. • The approach addresses single-objective optimization deficiencies. • It improves the existing average results in the scientific literature. • It generates more robust outcomes when repeating executions. [ABSTRACT FROM AUTHOR]

Abstract Prostate cancer is a highly incident malignant cancer among men. Early detection of prostate cancer is necessary for deciding whether a patient should receive costly and invasive biopsy with possible serious complications. However, existing cancer diagnosis methods based on data mining only focus on diagnostic accuracy, while neglecting the interpretability of the diagnosis model that is necessary for helping doctors make clinical decisions. To take both accuracy and interpretability into consideration, we propose a stacking-based ensemble learning method that simultaneously constructs the diagnostic model and extracts interpretable diagnostic rules. For this purpose, a multi-objective optimization algorithm is devised to maximize the classification accuracy and minimize the ensemble complexity for model selection. As for model combination, a random forest classifier-based stacking technique is explored for the integration of base learners, i.e., decision trees. Empirical results on real-world data from the General Hospital of PLA demonstrate that the classification performance of the proposed method outperforms that of several state-of-the-art methods in terms of the classification accuracy, sensitivity and specificity. Moreover, the results reveal that several diagnostic rules extracted from the constructed ensemble learning model are accurate and interpretable. Highlights • We propose a stacking-based interpretable selective ensemble learning method. • We select ensemble models with accuracy and complexity under consideration. • We combine selected effective models by random forest-based stacking. • The proposed method is more accurate and interpretable in prostate cancer detection. • We extract a few of effective diagnostic rules for clinical decision support. [ABSTRACT FROM AUTHOR]

Abstract Security-constrained optimal power flow (SCOPF) is an important problem in power system operation. Dynamic thermal rating (DTR), as an effective method to increase transmission capacity of power systems, has been recently considered in some optimal power flow (OPF) and SCOPF models. Additionally, in today power systems, OPF problem involves various objectives leading to multi-objective OPF models. In this paper, a new multi-objective SCOPF model considering DTR of transmission lines is presented. In addition, a new multi-objective solution method is proposed to solve the multi-objective SCOPF problem. The proposed method is an enhanced version of goal attainment technique in which the search capability of this technique to cover borders of the Pareto frontier is enhanced. The proposed multi-objective DTR-included SCOPF model as well as the proposed multi-objective solution method are tested on the IEEE 118-bus test system and the obtained results are compared with the results of other alternatives. Highlights • A new multi-objective DTR-included SCOPF model is presented. • A new multi-objective solution method is proposed. • Proposed method can search the beyond-utopia-hyperplane parts of Pareto frontier. • Effectiveness of the proposed model and proposed method is extensively evaluated. [ABSTRACT FROM AUTHOR]

Abstract The hybrid flowshop scheduling problem (HFSP) has been widely studied in the past decades. The most commonly used criterion is production efficiency. Green criteria, such as energy consumption and carbon emission, have attracted growing attention with the improvement of the environment protection awareness. Limited attention has been paid to noise pollution. However, noise pollution can lead to health and emotion disorder. Thus, this paper studies a multi-objective HFSP considering noise pollution in addition to production efficiency and energy consumption. First, we formulate a new mixed-integer programming model for this multi-objective HFSP. To realize the green scheduling, one energy conservation/noise reduction strategy is embedded into this model. Then, a novel multi-objective cellular grey wolf optimizer (MOCGWO) is proposed to address this problem. The proposed MOCGWO integrates the merits of cellular automata (CA) for diversification and variable neighborhood search (VNS) for intensification, which balances exploration and exploitation. Finally, to validate the efficiency and effectiveness of the proposed MOCGWO, we compare our proposal with other well-known multi-objective evolutionary algorithms by conducting comparison experiments. The experimental results show that the proposed MOCGWO is significantly better than its competitors on this problem. Highlights • A mathematical model for a HFSP considering noise pollution is formulated. • A multi-objective cellular grey optimizer is presented to solve this problem. • One energy conservation/noise reduction strategy is proposed. • A variable neighborhood search based on problem property is developed. [ABSTRACT FROM AUTHOR]

Abstract In this paper, a novel multi-objective optimization method based on a recently introduced algorithm known as Lightning Attachment Procedure Optimization (LAPO) is presented. The proposed algorithm is based on non-dominated sorting approach where the best solutions chosen from the Pareto Optimal Front (POF), based on crowding distance, are stored in a repository matrix called an Archive matrix. The procedure is performed such that the final best solutions are distributed evenly along the optimal PF. Then, the proposed algorithm is tested by some multi-objective optimization functions and some classical engineering problems also. The results are compared to those of four well-known methods and then discussed. The results are compared using 4 criteria which show how to select a POF close to the true POF, how the results are distributed, and how close the final results approximate all the possible outcomes of true POF. It is shown that the proposed method outperforms the other methods with regards to 3 criteria and yields comparable results regarding the last criteria. Superiority of the proposed method in finding the true POF while covering a wide range of possible optimal results is discussed in the results section. Therefore, it is concluded that the proposed method does an excellent job at solving a wide range of multi-objective optimization problems. Graphical abstract Highlights • In this paper a novel multi-objective optimization algorithm known as MOLAPO is proposed. • The Archive matrix is used to guarantee finding the best answers during the optimization. • According to the results the proposed method is highly recommended for the complicated multi-objective problems. [ABSTRACT FROM AUTHOR]

Combined heat and power economic emission dispatch (CHPEED) problem is a highly complex, non-linear, non-convex multi-objective optimization problem due to two conflicting objectives and various operational constraints such as valve-point loading effect, power transmission loss, prohibited operating zone, and the feasible operating region of combined heat and power unit. In order to overcome these challenges, it is necessary to design an algorithm that exhibits a search behavior, which is suitable for the characteristics of objective and constraint space of the CHPEED problem. For these reasons, a dynamic switched crowding based multi-objective symbiotic organism search (DSC-MOSOS) algorithm was designed to meet the requirements and geometric space of the CHPEED problem. By applying the DSC method in the MOSOS algorithm, it was aimed to improve the exploration ability, to strengthen exploitation-exploration balance, and to prevent the catching into local solution traps. A comprehensive experimental study was carried out to prove the performance of the proposed algorithm on IEEE CEC 2020 multi-modal multi-objective problems (MMOPs) and CHPEED problem. In the experimental study conducted among eleven versions of MOSOS variations created with DSC-method and the base MOSOS algorithm on IEEE CEC 2020 MMOPs, according to Friedman scores based on the four performance metrics, the base MOSOS algorithm ranked the last. In other experimental study, the best DSC-MOSOS variant was applied to solve the CHPEED problem, where 5-, 7-, 10- and 14-unit test systems and eight case studies were considered. The important points of this study were that 10-unit and 14-unit test systems were presented to the literature, and the prohibited operating zone was considered in CHPEED problem for the first time. According to the results obtained from eight case studies obtained from the DSC-MOSOS and fourteen competitor algorithms, while the improvement in cost was between 0.2% and 16.55%, the reduction of the emission value was between 0.2 kg and 42.97 kg compared to the competitor algorithms. On the other hand, the stability of the DSC-MOSOS and the base MOSOS was evaluated using stability analysis. While the MOSOS algorithms was not able to perform a success in any case study, the DSC-MOSOS was achieved an average success rate with 91.16%. Thus, the performance of the DSC-MOSOS over the MOSOS was verified by the results of experimental studies and analysis. • DSC-MOSOS algorithm was proposed to solve the multi-objective CHPEED problem. • VPLE, POZs, and transmission losses were considered in the CHPEED model. • New CHPEED test systems including 10- and 14-units were introduced. • It was tested on the IEEE CEC 2020 MMOPs and eight MO-CHPEED case studies. • Remarkable savings in fuel cost and reduction of emission levels were achieved. [ABSTRACT FROM AUTHOR]