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The optimization and allocation of transport cost savings among stakeholders are two important issues that influence the satisfaction of information providers, drivers and passengers in ridesharing recommendation systems. For optimization issues, finding optimal solutions for nonconvex constrained discrete ridesharing optimization problems poses a challenge due to computational complexity. For the allocation of transport cost savings issues, the development of an effective method to allocate cost savings in ridesharing recommendation systems is an urgent need to improve the acceptability of ridesharing. The hybridization of different metaheuristic approaches has demonstrated its advantages in tackling the complexity of optimization problems. The principle of the hybridization of metaheuristic approaches is similar to a marriage of two people with the goal of having a happy ending. However, the effectiveness of hybrid metaheuristic algorithms is unknown a priori and depends on the problem to be solved. This is similar to a situation where no one knows whether a marriage will have a happy ending a priori. Whether the hybridization of the Firefly Algorithm (FA) with Particle Swarm Optimization (PSO) or Differential Evolution (DE) can work effectively in solving ridesharing optimization problems needs further study. Motivated by deficiencies in existing studies, this paper focuses on the effectiveness of hybrid metaheuristic algorithms for solving ridesharing problems based on the hybridization of FA with PSO or the hybridization of FA with DE. Another focus of this paper is to propose and study the effectiveness of a new method to allocate ridesharing cost savings to the stakeholders in ridesharing systems. The developed hybrid metaheuristic algorithms and the allocation method have been compared with examples of several application scenarios to illustrate their effectiveness. The results indicate that hybridizing FA with PSO creates a more efficient algorithm, whereas hybridizing FA with DE does not lead to a more efficient algorithm for the ridesharing recommendation problem. An interesting finding of this study is very similar to what happens in the real world: "Not all marriages have happy endings". [ABSTRACT FROM AUTHOR]

Multifactorial optimization (MFO) is a kind of optimization problem that has attracted considerable attention in recent years. The multifactorial evolutionary algorithm utilizes the implicit genetic transfer mechanism characterized by knowledge transfer to conduct evolutionary multitasking simultaneously. Therefore, the effectiveness of knowledge transfer significantly affects the performance of the algorithm. To achieve positive knowledge transfer, this paper proposed an evolutionary multitasking optimization algorithm with adaptive transfer strategy based on the decision tree (EMT-ADT). To evaluate the useful knowledge contained in the transferred individuals, this paper defines an evaluation indicator to quantify the transfer ability of each individual. Furthermore, a decision tree is constructed to predict the transfer ability of transferred individuals. Based on the prediction results, promising positive-transferred individuals are selected to transfer knowledge, which can effectively improve the performance of the algorithm. Finally, CEC2017 MFO benchmark problems, WCCI20-MTSO and WCCI20-MaTSO benchmark problems are used to verify the performance of the proposed algorithm EMT-ADT. Experimental results demonstrate the competiveness of EMT-ADT compared with some state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

The communication infrastructure is an important part to provide the reliability for energy management in the smart grid environment. With the aim of reducing the infrastructure cost for residential energy management, this paper introduces a more complex multi-hop wireless remote metering network model. A novel evolutionary aggregation algorithm (EAA) is proposed to obtain the minimum number and locations of the local data centers (powerful nodes) in a 2-hop wireless remote metering network which has an arbitrary number of smart meters (ordinary nodes) with arbitrary transmission ranges. In the novel 2-hop EAA, the paper designs and implements two novel adaptive operations (the switch operation and the shuffle operation) to improve the algorithm performance. Then the paper extends the 2-hop EAA method to a more generic n-hop EAA which could obtain the optimal result in an n-hop (n>2) smart meter network. Comprehensive case studies and numerical statistical analyses demonstrate that the EAA could efficiently achieve the optimal results in an n-hop (n>=2) smart meter network environment and the novel switch and shuffle operations could efficiently improve the performance of the evolutionary algorithm. The connectivity of the smart meter network could be fulfilled with the minimum number of the powerful nodes, from which the infrastructure cost for residential energy network could be minimized. [ABSTRACT FROM AUTHOR]

In real-world applications, feature selection is crucial for enhancing the performance of data science and machine learning models. Typically, feature selection is a complex combinatorial optimization problem and a multi-objective optimization problem. Its primary goals are to reduce the dimensionality of the dataset and enhance the performance of machine learning algorithms. The selection of features in high-dimensional datasets is challenging due to the intricate relationships between features, which pose significant challenges to the performance and computational efficiency of algorithms. This paper introduces a Knowledge-Guided Competitive Co-Evolutionary Algorithm (KCCEA) for feature selection, especially for high-dimensional features. In the proposed algorithm, we make improvements to the foundational dominance-based multi-objective evolutionary algorithm in two aspects. First, the use of feature correlation as knowledge to guide evolution enhances the search speed and quality of traditional multi-objective evolutionary algorithm solutions. Second, a dynamically allocated competitive–cooperative evolutionary mechanism is proposed, integrating the improved knowledge-guided evolution with traditional evolutionary algorithms, further enhancing the search efficiency and diversity of solutions. Through rigorous empirical testing on various datasets, the KCCEA demonstrates superior performance compared to basic multi-objective evolutionary algorithms, providing effective solutions to multi-objective feature selection problems while enhancing the interpretability and effectiveness of prediction models. [ABSTRACT FROM AUTHOR]

The besiege and conquer algorithm has shown excellent performance in single-objective optimization problems. However, there is no literature on the research of the BCA algorithm on multi-objective optimization problems. Therefore, this paper proposes a new multi-objective besiege and conquer algorithm to solve multi-objective optimization problems. The grid mechanism, archiving mechanism, and leader selection mechanism are integrated into the BCA to estimate the Pareto optimal solution and approach the Pareto optimal frontier. The proposed algorithm is tested with MOPSO, MOEA/D, and NSGAIII on the benchmark function IMOP and ZDT. The experiment results show that the proposed algorithm can obtain competitive results in terms of the accuracy of the Pareto optimal solution. [ABSTRACT FROM AUTHOR]

Evolutionary algorithms rely on the population initialization strategy to determine a set of candidate solutions, which provide the preliminary knowledge of the problem landscape for the subsequent evolutionary process. However, the distribution of the initial population is rarely concerned in the multimodal evolutionary community. Moreover, a large initial population has not been attractive enough for evolutionary multimodal optimization, because it is difficult and challenging to achieve a balance between diversity and convergence within limited computing resources. As an extended version of our previous conference paper, this paper focuses on the construction of a large initial population that is beneficial for evolutionary multimodal optimization. First, we propose an improved density-based population initialization strategy to generate a uniform initial population with a certain degree of randomness. Then, after acquiring the raw fitness landscape of the problem, a fitness-weighted density-based population initialization strategy is proposed to explore potential global peaks. Finally, we design a multi-species cooperative coevolution algorithm for multimodal optimization, which balances the exploration and exploitation of such a population in the evolutionary process. Experimental results demonstrate that the proposed algorithm is better than the mainstream algorithms on the CEC2013 multimodal optimization benchmark. [ABSTRACT FROM AUTHOR]

In many seasonal industries, customer demands are constantly changing over time, and accordingly the facility layout should be re-optimized in a timely manner to adapt to changing material handling patterns among manufacturing departments. This paper investigates the aperiodic facility layout problem (AFLP) that involves arranging facilities layout and re-layout aperiodically in a dynamic manufacturing environment during a given planning horizon. The AFLP is decomposed into a master problem and a combination set of static facility layout problems (FLPs, the slave problems) without loss of optimality, and all problems are formulated as mixed-integer linear programming (MILP) models that can be solved by MIP solvers for small-sized problems. An exact backward dynamic programming (BDP) algorithm with a computational complexity of O(n2) is developed for the master problem, and an improved linear programming based problem evolution algorithm (PEA-LP) is developed for the traditional static FLP. Computational experiments are conducted on two new problems and twelve well-known benchmark problems from the literature, and the experimental results show that the proposed solution approach is promising for solving the AFLP with practical sizes of problem instances. In addition, the improved PEA-LP found new best solutions for five benchmark problems. [ABSTRACT FROM AUTHOR]

Two goals of multi-objective evolutionary algorithms are effectively improving their convergence and diversity and making the Pareto set evenly distributed and close to the real Pareto front. At present, the challenges to be solved by the multi-objective evolutionary algorithm are to improve the convergence and diversity of the algorithm, and how to better solve functions with complex PF and/or PS shapes. Therefore, this paper proposes a gray wolf optimization-based self-organizing fuzzy multi-objective evolutionary algorithm. Gray wolf optimization algorithm is used to optimize the initial weights of the self-organizing map network. New neighborhood relationships for individuals are built by self-organizing map, which can maintain the invariance of feature distribution and map the structural information of the current population into Pareto sets. Based on this neighborhood relationship, this paper uses the fuzzy differential evolution operator, which constructs a fuzzy inference system to dynamically adjust the weighting parameter in the differential operator, to generate a new initial solution, and the polynomial mutation operator to refine them. Boundary processing is then conducted. Experiments on 15 problems of GLT1-6 and WFG1-9 and the algorithm proposed in this paper achieve the best on 18 values. And the result shows that the convergence and diversity of the proposed algorithm are better than several state-of-the-art multi-objective evolutionary algorithms. [ABSTRACT FROM AUTHOR]

The multi-objective evolutionary algorithm optimization (MOEA) is a challenging but critical approach for tackling complex data analysis problems. However, prevailing MOEAs often rely on single strategies to obtain optimal solutions, leading to concerns such as premature convergence and insufficient population diversity, particularly in high-dimensional data scenarios. In this paper, we propose a novel adversarial population generation algorithm, APG-SMOEA, which synergistically combines the benefits of MOEAs and Generative Adversarial Networks (GANs) to address these limitations. In order to balance the efficiency and quality of offspring selection, we introduce an adaptive population entropy strategy, which includes control parameters based on population entropy and a learning pool for storing and retrieving optimal solutions. Additionally, we attempt to alleviate the training complexity and model collapse problems common in GANs with APG-SMOEA. Experimental results on benchmarks demonstrate that the proposed algorithm is superior to the existing algorithms in terms of solution quality and diversity of low-dimensional or high-dimensional complex data. [ABSTRACT FROM AUTHOR]

Evolutionary multi-objective multi-task optimization is an emerging paradigm for solving multi-objective multi-task optimization problem (MO-MTOP) using evolutionary computation. However, most existing methods tend to directly treat the multiple multi-objective tasks as different problems and optimize them by different populations, which face the difficulty in designing good knowledge transferring strategy among the tasks/populations. Different from existing methods that suffer from the difficult knowledge transfer, this paper proposes to treat the MO-MTOP as a multi-objective multi-criteria optimization problem (MO-MCOP), so that the knowledge of all the tasks can be inherited in a same population to be fully utilized for solving the MO-MTOP more efficiently. To be specific, the fitness evaluation function of each task in the MO-MTOP is treated as an evaluation criterion in the corresponding MO-MCOP, and therefore, the MO-MCOP has multiple relevant evaluation criteria to help the individual selection and evolution in different evolutionary stages. Furthermore, a probability-based criterion selection strategy and an adaptive parameter learning method are also proposed to better select the fitness evaluation function as the criterion. By doing so, the algorithm can use suitable evaluation criteria from different tasks at different evolutionary stages to guide the individual selection and population evolution, so as to find out the Pareto optimal solutions of all tasks. By integrating the above, this paper develops a multi-objective multi-criteria evolutionary algorithm framework for solving MO-MTOP. To investigate the proposed algorithm, extensive experiments are conducted on widely used MO-MTOPs to compare with some state-of-the-art and well-performing algorithms, which have verified the great effectiveness and efficiency of the proposed algorithm. Therefore, treating MO-MTOP as MO-MCOP is a potential and promising direction for solving MO-MTOP. [ABSTRACT FROM AUTHOR]

Many researchers witness the efficiency of decomposition-based evolutionary multi- and many-objective optimization algorithms in finding quality solutions on problems with regular Pareto front. These decomposition-based evolutionary approaches profoundly depend on aggregation methods and weight vectors. Such algorithms fail to guarantee the diversity in the population, especially when dealing with irregular Pareto front, because some of the weight vectors become ineffective during simulation. Efforts are being made over time to frequently adopt the position of weight vectors at the rate of increased computation burden, in order to achieve improved solutions. Hence an alternative mechanism has been suggested in this paper. Instead of changing the weight vector, a better treatment on environmental selection is proposed that could effectively balance both convergence and diversity in solving both multi- and many-objective optimization problems. The higher rate of performance of the proposed algorithm has been validated over the comparative permanence with six popular state-of-the-art algorithms in terms of hypervolume, complemented with winning ratio using t-test. It is concluded in the latter part of this paper that the proposed method performs better while solving test suite problems with irregular Pareto fronts. Moreover, it is more prone towards better solutions with an increase in the number of objective functions in MaOPs. [ABSTRACT FROM AUTHOR]

The main objective of this paper is to present a new 2-stage hybrid optimization algorithm based scheme named PSO-BHA for image enhancement. A parameterized mapping function and a novel objective function are utilized in this paper to achieve the best-enhanced images. The suggested scheme combines the merits of particle swarm optimization (PSO) with the black hole algorithm (BHA) in two sequential stages to find the best parameters for the mapping function with the aid of the proposed objective function. The objective function uses contrast, edge, entropy, and universal quality index (UQI) for measuring contrast, and different improved information in the enhanced image. In the proposed scheme, PSO is applied first to adjust the tunable parameters of the mapping function and as a result, new pixel intensities are produced. Then, in the second stage, the obtained pixel intensities are again passed through the mapping function whose parameters are tuned by the use of the BHA. The suggested framework overcomes the limitations of the traditional histogram equalization (HE) based enhancement techniques in which excessive contrast enhancement and image information loss can occur. The suggested method is evaluated on several test images and compared with different state-of-the-art methods. The results indicate that the proposed framework provides superior performance to all existing methods in terms of various metrics. The proposed scheme also contributes to substantial feature enhancement and contrast boosting in the enhanced image, while retaining the natural feel of the original image. [ABSTRACT FROM AUTHOR]

Solving many‐objective optimization problems (MaOPs) by means of evolutionary algorithms obtains considerable attention in the community of evolutionary computation. However, it is a difficult task to effectively handle MaOPs with both regular and irregular Pareto front (PF) shapes. In this paper, a novel two reference vector sets based evolutionary algorithm, referred to as TVEA, is proposed to solve both types of MaOPs. TVEA can be featured as i) two independent reference vector sets are used, where the regular reference vector set is used for the regular PF MaOPs and the irregular one is used for the irregular PF MaOPs; ii) according to the two reference vector sets, TVEA automatically detects the PF shape of a MaOP at hand; iii) a new delete‐and‐add strategy is proposed to adaptively update the irregular reference vector set; iv) a novel environmental selection strategy is developed to handle the irregular PF MaOPs. TVEA is extensively evaluated on 23 benchmark MaOPs with both regular and irregular PF shapes. The results indicate that TVEA can correctly detect the PF shape of MaOPs to be solved. Compared with other state‐of‐the‐art algorithms, TVEA obtains better or at least competitive results on both types of MaOPs. [ABSTRACT FROM AUTHOR]

The multiple knapsack problem（MKP） is a special knapsack problem with great difficulty. In order to solve MKP by differential evolution（DE）, this paper first established the integer programming model of MKP. Based on a simple and efficient new transfer function based on modulo operation, it proposed a novel discrete differential evolution algorithm MODDE. Then, the method used an efficient algorithm GROA to eliminate the unfeasible solution of MKP by greedy strategy. Therefrom, this paper proposed a new method for solving MKP based on MODDE. Finally, it used MODDE to solve 30 international instances of MKP. Comparing with 4 representative evolution algorithms shows that MODDE not only has better calculation results; but also has stronger stability. It is indeed an efficient algorithm for solving MKP [ABSTRACT FROM AUTHOR]

This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where multiple UAVs are used to serve mobile users. We aim to minimize the overall energy consumption of the system by planning the trajectories of UAVs. To plan the trajectories of UAVs, we need to consider the deployment of hovering points (HPs) of UAVs, their association with UAVs, and their order for each UAV. Therefore, the problem is very complicated, as it is non-convex, nonlinear, NP-hard, and mixed-integer. To solve the problem, this paper proposed an evolutionary trajectory planning algorithm (ETPA), which comprises four phases. In the first phase, a variable-length GA is adopted to update the deployments of HPs for UAVs. Accordingly, redundant HPs are removed by the remove operator. Subsequently, a differential evolution clustering algorithm is adopted to cluster HPs into different clusters without knowing the number of HPs in advance. Finally, a GA is proposed to construct the order of HPs for UAVs. The experimental results on a set of eight instances show that the proposed ETPA outperforms other compared algorithms in terms of the energy consumption of the system. [ABSTRACT FROM AUTHOR]

This paper proposes the Love Evolution Algorithm (LEA), a novel evolutionary algorithm inspired by the stimulus–value–role theory. The optimization process of the LEA includes three phases: stimulus, value, and role. Both partners evolve through these phases and benefit from them regardless of the outcome of the relationship. This inspiration is abstracted into mathematical models for global optimization. The efficiency of the LEA is validated through numerical experiments with CEC2017 benchmark functions, outperforming seven metaheuristic algorithms as evidenced by the Wilcoxon signed-rank test and the Friedman test. Further tests using the CEC2022 benchmark functions confirm the competitiveness of the LEA compared to seven state-of-the-art metaheuristics. Lastly, the study extends to real-world problems, demonstrating the performance of the LEA across eight diverse engineering problems. Source codes of the LEA are publicly available at https://ww2.mathworks.cn/matlabcentral/fileexchange/159101-love-evolution-algorithm. [ABSTRACT FROM AUTHOR]

Routing of vehicles and scheduling of deliveries play a crucial role in logistics operations as they affect both the distribution cost and customer satisfaction. That is why researchers have intensively studied this problem in conjunction with the multiple variables and constraints involved in the logistics operations. In this paper, the cases of time windows and simultaneous pickups and deliveries, where goods are simultaneously delivered and collected from customers within a predetermined time slot, are studied. The objective of our research is to create efficient routes that minimize both the number of vehicles and the total distance travelled, as both of them affect the total distribution cost. Considering various plans of routes that are differentiated by the number of routes and the sequence of visitations, can be beneficial for decision-makers, since they have the opportunity to select the plan that better fits their needs. Therefore, in this paper we develop a multiobjective evolutionary algorithm (MOEA) that integrates an improved construction algorithm and a new crossover operator for efficient distribution services. Through the proposed MOEA a set of solutions (route plans), known as Pareto-optimal, is obtained, while single biased solutions are avoided. The proposed algorithm is tested in two well-known datasets in order to evaluate the algorithm's efficiency. The results indicate that the algorithm's solutions have small deviation from the best-published and some non-dominated solutions are also obtained. [ABSTRACT FROM AUTHOR]

The maximum internal spanning tree (MIST) problem is to find a spanning tree with maximum number of internal node for an undirected graph. It is a variation of the well-known minimum spanning tree problem and is NP-hard. Evolutionary algorithms (EAs) have been successfully applied to solve many NP-hard combinatorial optimization problems in numerical empirical studies. However, researchers know little about their performance guarantees from theory aspect. This paper designs a valid fitness function to guide the well-studied evolutionary algorithm, (1 + 1) EA , to optimize the MIST problem, and presents theoretical analysis to show that it can obtain a performance guarantee of 2 and 5/3 in expected runtime O (n m 2) and O (n m 4) , respectively. Moreover, this paper proves that the (1+1) EA can achieve a performance guarantee of 3 for a variation of the MIST problem, where each vertex is associated with a weight. In addition, comparison analyses on two family instance graphs are presented to show that (1 + 1) EA is better than two local search algorithms. This theoretical study provides insight into the process of (1 + 1) EA constructing a performance guarantee solution for the MIST problem. [ABSTRACT FROM AUTHOR]

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The previous studies on scheduling problem with tool changes take processing time as the only reason for the tool wear, which is not accurate in the real manufacturing system. This paper takes processing speed and processing time into consideration simultaneously and proposes a new unrelated parallel machine scheduling problem (UPMSP) with tool changes caused by the tool wear, in which the energy consumption rate of the parallel machines is influenced by two factors: tool changes and corresponding processing speed. A new effective heuristic evolutionary algorithm (NHEA) is presented to solve the proposed UPMSP with objectives of optimising total energy consumption and makespan. For the NHEA, some effective operators such as target-searching operators are designed to accelerate the search efficiency and further exploit the solution space. A first fit decreasing algorithm is presented and incorporated into the NHEA to reduce the number of tool changes. The Taguchi method of Design of Experiments is used to obtain the best combination of key parameters of the NHEA. Extensive computational experiments are carried out to compare the NHEA with some well-known algorithms. The results validate that the proposed NHEA is able to obtain better Pareto solutions for UPMSP with tool changes. [ABSTRACT FROM AUTHOR]

The weapon-target allocation (WTA) problem is a fundamental subject of defense-related applications research, and previous studies assume that the parameters in the model are determinate. For the real battlefield, asymmetric information usually leads to the failure of the above assumption, and there are uncertain factors whose frequency is hard to pinpoint. Based on uncertainty theory, we study a WTA problem in indeterminate battlefield in this paper. First, we analyze the uncertain factors in indeterminate battlefield and their influence on WTA problem. Then, considering the target threat value, the protected asset value and the extra cost of interception as uncertain variables, the uncertain multi-objective dynamic WTA (UMDWTA) model is established, where three indices including the value of destruction of targets, the value of surviving assets and the cost of operation are regarded as objective functions, and on this basis, an equivalent transformation is presented to convert the UMDWTA model into a determinate multi-objective programming (MOP) problem by expected value and standard deviation principle. To solve the proposed model efficiently, an improved multi-objective evolutionary algorithm based on decomposition (MOEA/D) is designed, which employs three new evolutionary operators and the weight vectors adaptation mechanism to improve the convergence and uniformity of the Pareto front obtained. Finally, a case of the UMDWTA problem is carried out to be solved by the designed algorithm, and the results verify the feasibility of the proposed model. [ABSTRACT FROM AUTHOR]

Radio frequency identification (RFID) is a widespread technology used in several different industries. One of its common use cases in logistics is the automation of goods receipt. RFID gates are often deployed, to automatically detect tagged items or load carriers during their passage through the goods receipt gate. At present, however, the design of RFID gates is often based on estimations, and their commissioning is mostly approached via trial and error. Even if the RFID network planning problem is known in the literature, existing algorithms cannot be applied to the design of RFID gates due to some limitations. In this paper, we propose a new evolutionary RFID network planning algorithm to design RFID gates optimally. The objective of our algorithm is to minimise the number of antennas and to adjust their mounting heights and angles. The algorithm ensures a tag coverage of at least 99%, prevents reflections on the ground, and can be used in the future as a standard for planning and commissioning RFID-enabled goods receipt gates. To demonstrate the applicability of our algorithm, we deployed it in a case study involving logistics of the automotive sector. The results of the deployment confirm the quality of our approach, as the RFID gate optimised by the algorithm deployed 4 antennas, with a vertical coverage rate of 99.96%, an horizontal coverage rate of 89.66%, and very interesting values of other evaluation functions, namely load balance and overlapping rate. [ABSTRACT FROM AUTHOR]

The design of genetic operators is absolutely one of the core work of evolutionary algorithms research. However, the essence of the evolutionary algorithms is that a lot of algorithm design is based on the manual result analysis, summarize, refine, feedback, and then, the algorithms are designed adaptively and correspondingly. This kind of design scheme needs artificial statistics and analysis of large amounts of data, which greatly increases the burden of the designers. To solve this problem, an evolutionary algorithm framework based on genetic operator automatic design is proposed in this paper. In the first step, Gene Expression Programming and Differential Evolution methods are combined together and used to design the genetic operators automatically and adaptively, this hybrid method can not only explore solutions in problem space for the problem solving as most classical evolutionary algorithms do, but also generate genetic operators automatically in operator space for the proper operators extraction and selection related to the evolutionary algorithms. In the second step, the designed operators are adopted into the typical evolutionary algorithms to verify the performance and the result shows that the new designed genetic operator is superior to or at least equivalent to some existing DE variants in a set of classical benchmark functions. More importantly, this paper is not aimed at designing high performance algorithms, but to provide a new perspective for algorithms designing, and to provide a reference scheme for the machine algorithms designing. [ABSTRACT FROM AUTHOR]

Maintaining a good balance between convergence and diversity is crucial in many-objective optimization, while most existing dominance relations can not achieve a good balance between them. In this paper, we propose a new dominance relation to better balance the convergence and diversity. In the proposed dominance relation, a convergence indicator and a niching technique based adaptive parameter are adopted to ensure the convergence and diversity of the nondominated solution set. Based on the proposed dominance relation, a new many-objective evolutionary algorithm is proposed. In the algorithm, a new distribution estimation method is proposed to obtain better solutions for mating selection. Experimental results indicate that the proposed dominance relation outperforms existing dominance relations in balancing the convergence and diversity and the proposed algorithms has a competitive performance against several state-of-art many-objective evolutionary algorithms. [ABSTRACT FROM AUTHOR]

The partner selection problem (PSP) is a key issue in constituting and reconfiguring strategic alliances. In this paper, we seek to address PSP under time, budget, activity precedence, and resource constraints. Multiple objectives are considered, our proposed approach simultaneously minimizing total cost and project duration while maximizing average quality. For these purposes, we present a novel multi-objective symbiotic organisms search for scheduling (MOSOSS). In this new algorithm, evolutionary operators are completely redesigned for combinatorial optimization. Furthermore, they are specifically adapted for scheduling problems. One notable original aspect of the new MOSOSS algorithm is that it evolves partial (incompletely scheduled) solutions. For this purpose, we propose evolutionary operators specially constructed to deal with both incomplete and complete schedules. Experimental results on randomly generated PSP instances show that MOSOSS offers a better coverage of the Pareto front as compared to the extant multiple objective symbiotic organisms search and NSGA-II. [ABSTRACT FROM AUTHOR]

Numerous surrogate-assisted expensive multi-objective optimization algorithms were proposed to deal with expensive multi-objective optimization problems in the past few years. The accuracy of the surrogate models degrades as the number of decision variables increases. In this paper, we propose a surrogate-assisted expensive multi-objective optimization algorithm based on decision space compression. Several surrogate models are built in the lower dimensional compressed space. The promising points are generated and selected in the lower compressed decision space and decoded to the original decision space for evaluation. Experimental studies show that the proposed algorithm achieves a good performance in handling expensive multi-objective optimization problems with high-dimensional decision space. [ABSTRACT FROM AUTHOR]

With the increase in the number of optimization objectives, balancing the convergence and diversity in evolutionary multiobjective optimization becomes more intractable. So far, a variety of evolutionary algorithms have been proposed to solve many-objective optimization problems (MaOPs) with more than three objectives. Most of the existing algorithms, however, find difficulties in simultaneously counterpoising convergence and diversity during the whole evolutionary process. To address the issue, this paper proposes to solve MaOPs via multistage evolutionary search. To be specific, a two-stage evolutionary algorithm is developed, where the convergence and diversity are highlighted during different search stages to avoid the interferences between them. The first stage pushes multiple subpopulations with different weight vectors to converge to different areas of the Pareto front. After that, the nondominated solutions coming from each subpopulation are selected for generating a new population for the second stage. Moreover, a new environmental selection strategy is designed for the second stage to balance the convergence and diversity close to the Pareto front. This selection strategy evenly divides each objective dimension into a number of intervals, and then one solution having the best convergence in each interval will be retained. To assess the performance of the proposed algorithm, 48 benchmark functions with 7, 10, and 15 objectives are used to make comparisons with five representative many-objective optimization algorithms. [ABSTRACT FROM AUTHOR]

In the paradigm of industry 4.0, manufacturing enterprises need a high level of agility to adapt fast and with low costs to small batches of diversified products. They also need to reduce the environmental impact and adopt the paradigm of the circular economy. In the configuration space defined by this duality, manufacturing systems must embed a high level of reconfigurability at the level of their equipment. Finding the most appropriate concept of each reconfigurable equipment that composes an eco-smart manufacturing system is challenging because every system is unique in the context of an enterprise's business model and technological focus. To reduce the entropy and to minimize the loss function in the design process of reconfigurable equipment, an evolutionary algorithm is proposed in this paper. It combines the particle swarm optimization (PSO) method with the theory of inventive problem-solving (TRIZ) to systematically guide the creative potential of design engineers towards the definition of the optimal concept over equipment's lifecycle: what and when you need, no more, no less. The algorithm reduces the number of iterations in designing the optimal solution. An example for configuration design of a reconfigurable machine tool with adjustable functionality is included to demonstrate the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Optical character recognition (OCR) systems help to digitize paper-based historical achieves. However, poor quality of scanned documents and limitations of text recognition techniques result in different kinds of errors in OCR outputs. Post-processing is an essential step in improving the output quality of OCR systems by detecting and cleaning the errors. In this paper, we present an automatic model consisting of both error detection and error correction phases for OCR post-processing. We propose a novel approach of OCR post-processing error correction using correction pattern edits and evolutionary algorithm which has been mainly used for solving optimization problems. Our model adopts a variant of the self-organizing migrating algorithm along with a fitness function based on modifications of important linguistic features. We illustrate how to construct the table of correction pattern edits involving all types of edit operations and being directly learned from the training dataset. Through efficient settings of the algorithm parameters, our model can be performed with high-quality candidate generation and error correction. The experimental results show that our proposed approach outperforms various baseline approaches as evaluated on the benchmark dataset of ICDAR 2017 Post-OCR text correction competition. [ABSTRACT FROM AUTHOR]

This paper presents an optimal design of a sweep blade for the axial wind turbine using a hybrid surrogate-assisted optimizer. The design problem is defined to maximize the ratio of the torque coefficient to the thrust coefficient of a turbine blade at a low wind velocity of 10 m/s. Pitch angle and leading-edge blade curve are considered as the design variables. For the aerodynamic analysis of the wind turbine blade, computational fluid dynamics has been used as a high-fidelity simulation. While the surrogate models including, the Kriging model (KG), the radial basis function model (RBF), and the proposed hybrid of KG and RBF (HyKG-RBF) models are applied for function approximation or low-fidelity simulation. In this study, to obtain a set of sampling points and surrogate models development, an optimal Latin Hypercube sampling (OLHS) technique is utilized in the design of the experiment (DOE). A differential evolutionary (DE) algorithm is used to solve the proposed design problem. The performance of the proposed hybrid surrogate assisted optimization method is contrasted with two conventional surrogate assisted optimization techniques. Results demonstrate that the proposed hybrid surrogate model viz. HyKG-RBF is the most efficient surrogate-assisted optimization method for solving the sweep blade optimization problem. [ABSTRACT FROM AUTHOR]

The self-organizing migrating algorithm (SOMA) is a population-based meta-heuristic that belongs to swarm intelligence. In the last 20 years, we can observe two main streams in the publications. First, novel approaches contributing to the improvement of its performance. Second, solving the various optimization problems. Despite the different approaches and applications, there exists no work summarizing them. Therefore, this work reviews the research papers dealing with the principles and application of the SOMA. The second goal of this work is to provide additional information about the performance of the SOMA. This work presents the comparison of the selected algorithms. The experimental results indicate that the best-performing SOMAs provide competitive results comparing the recently published algorithms. [ABSTRACT FROM AUTHOR]

Dynamic multiobjective optimization problems (DMOPs) challenge multiobjective evolutionary algorithms (MOEAs) because of the varying Pareto-optimal sets (POS) over time. Research on DMOPs has attracted a great interest from academic, due to widespread applications of DMOPs. Recently, a few learning-based approaches have been proposed to predict new solutions in the following environments as an initial population for a multiobjective evolutionary algorithm. In this paper, we propose an alternative learning-based method for DMOPs, a deep multi-layer perceptron-based predictor to generate an initial population for the MOEA in the new environment. The historical optimal solutions are used to train a deep multi-layer perceptron which then predicts a new set of solutions as the initial population in the new environment. The deep multi-layer perceptron is incorporated with the multiobjective evolutionary algorithm based on decomposition to solve DMOPs. Empirical results demonstrate that our proposed algorithm is effective in tracking varying solutions over time and shows great superiority comparing with state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Rao algorithms that include three algorithms are very simple and parameter-less algorithms with effective and desirable performance. This paper modifies these three algorithms, merges them, and establishes a powerful group algorithm. In the first optimization step, the suggested algorithm is tested on 30 standard CEC2014 functions with 50 dimensions to compare it with main algorithms, several well-known algorithms, and modified versions of RAO algorithm. It becomes evident in the first test that the suggested optimizer is effective, and reliable for optimization of real-parameter functions, and it has shown its superiority to original RAO algorithm and several modern and modified versions of RAO algorithms for most of the test functions and achieved more acceptable results than them. Moreover, the suggested algorithm benefits a faster convergence characteristic than original RAO algorithms. The proposed Colonial Competitive RAO (CCRAO) has been applied on five popular engineering problems and its results have been compared with those of recent papers. According to the results, CCRAO is an effective, robust, and reliable optimizer for engineering design problems and can contain all useful features of RAO algorithms altogether. CCRAO has succeeded to converge to the best solution for these engineering problems and surpasses most of the other algorithms. [ABSTRACT FROM AUTHOR]

Randomized search heuristics such as evolutionary algorithms are frequently applied to dynamic combinatorial optimization problems. Within this paper, we present a dynamic model of the classic weighted vertex cover problem and analyze the runtime performances of the well-studied algorithms randomized local search and (1 + 1) EA adapted to it, to contribute to the theoretical understanding of evolutionary computing for problems with dynamic changes. In our investigations, we use an edge-based representation based on the dual form of the Linear Programming formulation for the problem and study the expected runtime that the adapted algorithms require to maintain a 2-approximate solution when the given weighted graph is modified by an edge-editing or weight-editing operation. Considering the weights on the vertices may be exponentially large with respect to the size of the graph, the step size adaption strategy is incorporated, with or without the 1/5-th rule that is employed to control the increasing/decreasing rate of the step size. Our results show that three of the four algorithms presented in the paper can recompute 2-approximate solutions for the studied dynamic changes in polynomial expected runtime, but the (1 + 1) EA with 1/5-th rule requires pseudo-polynomial expected runtime. [ABSTRACT FROM AUTHOR]

The software-defined networks-enable mobile edge computing (SDN-enable MEC) architecture, which integrates SDN and MEC technologies, realizes the flexibility and dynamic management of the underlying network resources by the MEC, reduces the distance between the access terminal and computing resources and network resources, and increases the terminal's access to resources. However, the static distribution relationship between MEC servers (MECSs) and controllers in the multi-controller architecture may result in unbalanced load distribution among the controllers, which would degrade network performance. In this paper, a multi-objective optimization MECS redistribution algorithm (MOSRA) is proposed to decrease the response time and overhead. A controller response time model and link transmit overhead model are introduced as basis of an evolutionary algorithm which is proposed to optimize MECS redistribution. The proposed algorithm aims to select an available sub-optimizes individual by using a strategy based coordination transformation from Pareto Front. That is, when the master controller of the MECS is redistributed, both of the network overhead of the MECS to the controller and the response time of the controller to the MECS processing request are optimized. Finally, the simulation results demonstrate that the MOSRA can solve the redistribution problem in different network load levels and different network sizes within the effective time, and has a lower control plane response time, while making the edge network plane transmission overhead lower, compared with other algorithms. [ABSTRACT FROM AUTHOR]

This paper presents two novel swarm intelligence algorithms for gene selection, HHO-SVM and HHO-KNN. Both of these algorithms are based on Harris Hawks Optimization (HHO), one in conjunction with support vector machines (SVM) and the other in conjunction with k-nearest neighbors (k-NN). In both algorithms, the goal is to determine a small gene subset that can be used to classify samples with a high degree of accuracy. The proposed algorithms are divided into two phases. To obtain an accurate gene set and to deal with the challenge of high-dimensional data, the redundancy analysis and relevance calculation are conducted in the first phase. To solve the gene selection problem, the second phase applies SVM and k-NN with leave-one-out cross-validation. A performance evaluation was performed on six microarray data sets using the two proposed algorithms. A comparison of the two proposed algorithms with several known algorithms indicates that both of them perform quite well in terms of classification accuracy and the number of selected genes. [ABSTRACT FROM AUTHOR]

Many real-world problems can be modeled as dynamic multiobjective optimization ones with several competing objectives, which requires an optimization algorithm to track the movement of Pareto front over time. This paper proposes a novel dynamic diversity introduction strategy based on change intensity to improve the performance of dynamic multiobjective optimization based on evolutionary algorithm (DMOEA). In this proposed method, the information generated during evolution is recorded in preparation for evaluating the change intensity. Then, by comparing the evaluated intensity with the inherent intensity, the introduction ratio can be determined by that greater one. Two diversity introduction strategies are utilized to keep the balance between convergence and diversity when environmental change is detected. An improved inverse modeling is used for those drastic changes, while partial solutions random initialization is utilized for these mild ones. We compare the proposed algorithm with four existing DMOEAs on a variety of test instances. The experimental results show that the proposed algorithm exhibits better search performance. [ABSTRACT FROM AUTHOR]

Hybridized algorithms are commonly employed to improve the performance of any existing method. However, an optimal learning algorithm composed of evolutionary and swarm intelligence can radically improve the quality of the final neuron states and has not received creative attention yet. Considering this issue, this paper presents a novel metaheuristics algorithm combined with several objectives—introduced as the Hybrid Election Algorithm (HEA)—with great results in solving optimization and combinatorial problems over a binary search space. The core and underpinning ideas of this proposed HEA are inspired by socio-political phenomena, consisting of creative and powerful mechanisms to achieve the optimal result. A non-systematic logical structure can find a better phenomenon in the study of logic programming. In this regard, a non-systematic structure known as Random k Satisfiability (RANkSAT) with higher-order is hosted here to overcome the interpretability and dissimilarity compared to a systematic, logical structure in a Discrete Hopfield Neural Network (DHNN). The novelty of this study is to introduce a new multi-objective Hybrid Election Algorithm that achieves the highest fitness value and can boost the storage capacity of DHNN along with a diversified logical structure embedded with RANkSAT representation. To attain such goals, the proposed algorithm tested four different types of algorithms, such as evolutionary types (Genetic Algorithm (GA)), swarm intelligence types (Artificial Bee Colony algorithm), population-based (traditional Election Algorithm (EA)) and the Exhaustive Search (ES) model. To check the performance of the proposed HEA model, several performance metrics, such as training–testing, energy, similarity analysis and statistical analysis, such as the Friedman test with convergence analysis, have been examined and analyzed. Based on the experimental and statistical results, the proposed HEA model outperformed all the mentioned four models in this research. [ABSTRACT FROM AUTHOR]

An important goal in template-free protein structure prediction is how to control the quality of computed tertiary structures of a target amino-acid sequence. Despite great advances in algorithmic research, given the size, dimensionality, and inherent characteristics of the protein structure space, this task remains exceptionally challenging. It is current practice to aim to generate as many structures as can be afforded so as to increase the likelihood that some of them will reside near the sought but unknown biologically-active/native structure. When operating within a given computational budget, this is impractical and uninformed by any metrics of interest. In this paper, we propose instead to equip algorithms that generate tertiary structures, also known as decoy generation algorithms, with memory of the protein structure space that they explore. Specifically, we propose an evolving, granularity-controllable map of the protein structure space that makes use of low-dimensional representations of protein structures. Evaluations on diverse target sequences that include recent hard CASP targets show that drastic reductions in storage can be made without sacrificing decoy quality. The presented results make the case that integrating a map of the protein structure space is a promising mechanism to enhance decoy generation algorithms in template-free protein structure prediction. [ABSTRACT FROM AUTHOR]

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Recently, various research fields have begun dealing with massive datasets for several functions. The main aim of a feature selection (FS) model is to eliminate noise, repetitive, and unnecessary features that reduce the efficiency of classification. In a limited period, traditional FS models cannot manage massive datasets and filter unnecessary features. It has been discovered from the state-of-the-art literature that metaheuristic algorithms perform better compared to other FS wrapper-based techniques. Common techniques such as the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) algorithm, however, suffer from slow convergence and local optima problems. Even with new generation algorithms such as Firefly heuristic and Fish Swarm Heuristic, these questions have been shown to overcome. This paper introduces an improved memetic optimization (EMO) algorithm for FS in this perspective by using conditional criteria in large datasets. The proposed EMO algorithm divides the entire dataset into sample blocks and conducts the task of learning in the map steps. The partial result obtained is combined into a final vector of feature weights in the reduction process which defines the appropriate collection of characteristics. Finally, the method of grouping based on the support vector machine (SVM) takes place. Within the Spark system, the proposed EMO algorithm is applied and the experimental results claim that it is superior to other approaches. The simulation results show that the maximum AUC values of 0.79 and 0.74 respectively are obtained by the EMO-FS model. [ABSTRACT FROM AUTHOR]

Summary: In the last decade, the scale of heterogeneous computing (HC) systems such as heterogeneous cloud computing environments was growing like never before. So network failures are unavoidable in such systems, which affect system reliability. Since the task scheduling algorithm in HC is challenging, we investigate a new reliability‐aware task scheduling algorithm (RATSA) in this paper. RATSA is designed to schedule tasks on directed acyclic graphs (DAGs) by using the shuffled frog‐leaping algorithm (SFLA) and genetic algorithm (GA) as evolutionary algorithms. The population‐based SFLA‐GA is applied to optimize makespan in the RATSA as an NP‐complete problem. Moreover, the proposed algorithm exploits a new heuristic‐based earliest finish time technique for task mapping to virtual machines (VMs) section to decrease the failure rate. Experimental results on random DAGs indicate that RATSA improves some current algorithms in terms of reliability and has acceptable performance in makespan. The results reveal that the RATSA decreases the overall failure rate by 43% compared to some current task scheduling algorithms. [ABSTRACT FROM AUTHOR]

Many-objective optimization problems (MaOPs) are challenging tasks involving optimizing many conflicting objectives simultaneously. Decomposition-based many-objective evolutionary algorithms have effectively maintained a balance between convergence and diversity in recent years. However, these algorithms face challenges in accurately approximating the complex geometric structure of irregular Pareto fronts (PFs). In this paper, an information entropy-driven evolutionary algorithm based on reinforcement learning (RL-RVEA) for many-objective optimization with irregular Pareto fronts is proposed. The proposed algorithm leverages reinforcement learning to guide the evolution process by interacting with the environment to learn the shape and features of PF, which adaptively adjusts the distribution of reference vectors to cover the PFs structure effectively. Moreover, an information entropy-driven adaptive scalarization approach is designed in this paper to reflect the diversity of nondominated solutions, which facilitates the algorithm to balance multiple competing objectives adaptively and select solutions efficiently while maintaining individual diversity. To verify the effectiveness of the proposed algorithm, the RL-RVEA compared with seven state-of-the-art algorithms on the DTLZ, MaF, and WFG test suites and four real-world MaOPs. The results of the experiments demonstrate that the suggested algorithm provides a novel and practical method for addressing MaOPs with irregular PFs. • A novel RL-RVEA addresses many-objective optimization with irregular pareto fronts. • A reinforcement learning-based adaptive reference vector to guide the direction of convergence. • A scalarization approach preserves the diversity of solutions for next generation. • The RL-RVEA outperforms seven advanced many-objective optimization algorithms. [ABSTRACT FROM AUTHOR]

Based on the dual-inheritance framework of cultural evolution, an improved multiobjective cultural algorithm (IMOCA) with a multistrategy knowledge base is presented in this paper. Inspired by the original versions of the cultural algorithm (CA), four basic types of knowledge sources, i.e., normative, situational, topographical and historical knowledge, are effectively utilized in the proposed IMOCA. Several modifications with the knowledge base of the IMOCA are made to tackle the characteristics of the multiobjective problem. Situational knowledge is used as an external repository for storing elite individuals, and the redesigned topographical knowledge functions as a search engine to broaden the expansion of the obtained solution set. The historical knowledge used in the IMOCA aims to select a productive knowledge source to generate new individuals. Furthermore, a simple mutation scheme is introduced into the knowledge base as an influence function for the purpose of fine tuning in the late stage of search. After configuring the parameters used in IMOCA, two classic benchmark suites, i.e., WFG and MaF, are used to assess the performance of the IMOCA in approaching the Pareto fronts (PFs) with accuracy and diversity. Nondominated solution sets obtained by the IMOCA are compared with 8 state-of-the-art multiobjective algorithms available in the literature. A statistical analysis is conducted, which reveals that, by modifying the basic knowledge structure of the CA, the proposed multiobjective cultural algorithm is competent enough to handle multiobjective problems with competitive performance. [ABSTRACT FROM AUTHOR]

In this paper, the modified Spider Monkey Optimization (SMO) with Multi-Layer Perceptron (MLP) is utilized to solve the classification problem on five different datasets. The MLP is a widely used Neural Network (NN) variant which requires training on specific application to tackle the slow convergence speed and local minima avoidance. The original SMO with MLP experiences the problem of finding the optimal classification result; due to that, the SMO is enhanced by other meta-heuristics algorithm to train the MLP. Based on the concept of no free lunch theorem, there is always a possibility to improve the algorithm. With the same expectation, the performance of the SMO algorithm is improved by using Differential Evolution (DE) and Grey Wolf Optimizer (GWO) algorithm to train the MLP. Likewise, the SMO-DE and SMO-GWO are two different concepts employed to improve efficiency. The results of proposed algorithms are compared with other well-known algorithms such as BBO, PSO, ES, SVM, KNN, and Logistic Regression. The results show that the proposed algorithm performs better than others or they are more competitive. [ABSTRACT FROM AUTHOR]

MOEA/D is one of the most popular multi-objective evolutionary algorithms. To extend the effective application scope of MOEA/D for high-dimensional objectives, an improved MOEA/D design for many-objective optimization problems, named I-MOEA/D, is proposed in this paper. Comparing with the original MOEA/D, we redesigned the weight vectors used in the subproblems, making the distribution of the weight vectors broader and more effective to ensure the diversity and convergence of solutions in the objective space. Moreover, a new decomposition approach, called the weighted mixture-style method, which combines the advantages of the weighted sum decomposition and the Tchebycheff decomposition approaches, is adopted in I-MOEA/D to improve the effectiveness of the algorithm. A three-part experimental comparison using DTLZ1-DTLZ4, with the number of objectives ranging from three to fifteen, is performed. Experimental results verify the effectiveness of each strategy and reveal that the proposed I-MOEA/D method achieves better performance than the other related state-of-the-art algorithms in solving this type of many-objective optimization problems. [ABSTRACT FROM AUTHOR]

Metaheuristic algorithms have been widely used to solve structural optimization problems. Despite their powerful search capabilities, these algorithms often require a large number of fitness evaluations. Constructing a machine learning classifier to identify which individuals should be evaluated using the original fitness evaluation is a great solution to reduce the computational cost. However, there is still a lack of a thorough comparison between machine learning classifiers when integrating into the optimization process. This paper aims to evaluate the efficiencies of different classifiers in eliminating unnecessary fitness evaluations. For this purpose, the weight optimization of a double-layer grid structure comprising 200 members is used as a numerical experiment. Six machine learning classifiers selected for assessment in this study include Artificial Neural Network, Support Vector Machine, k-Nearest Neighbor, Decision Tree, Random Forest, and Adaptive Boosting. The comparison is made in terms of the optimal weight of the structure, the rejection rate as well as the computing time. Overall, it is found that the AdaBoost classifier achieves the best performance. [ABSTRACT FROM AUTHOR]

Water distribution networks (WDNs) connect consumers to the source of water. The primary goal of optimizing WDNs is to minimize the network costs as WDNs entail high construction costs. This paper presents a newly developed parameter-less Rao algorithm for optimization of WDNs. The methodology is based on a pressure and discharge dependent penalty. It is written in python code by linking to a hydraulic model of WDN implemented in EPANET. The algorithm is applied and tested on three benchmark networks, namely Two-loop (TL), New York Tunnel (NYT) and Goyang (GY) and a real WDN of School of Planning and Architecture (SPA), Bhopal, India. Rao algorithm employs two approaches to hydraulic modelling, demand-driven analysis (DDA) and pressure-driven analysis (PDA), as the DDA-Rao algorithm and the PDA-Rao algorithm. PDA-Rao algorithm outperforms DDA-Rao algorithm in terms of convergence. PDA-Rao algorithm saved 1.7% network cost for the NYT network, while the best-known least-cost values were obtained for TL and GY networks. It is seen that the Rao algorithms are efficient, easy to apply and do not require any parameter tuning, which reduces a large number of computational efforts. [ABSTRACT FROM AUTHOR]

The design of an electromagnetic machine is a nonlinear, multivariable, and multimodal optimization problem that incurs a great deal of computation time when calculating electromagnetic fields. To overcome these problems effectively, this paper proposes a new evolutionary multimodal optimization algorithm based on the Big Bang–Big Crunch method and aided by a surrogate model using the theory of compressed sensing. Its efficiency is demonstrated by assessing the optimization results for test functions. Moreover, to evaluate the feasibility of its application to an electromagnetic problem, an interior permanent magnet motor is designed using the proposed algorithm. The obtained results confirm that the proposed method has the ability to search for multiple optimal solutions with a good computational efficiency by reducing the number of fitness function evaluations during the optimization process. [ABSTRACT FROM AUTHOR]