Search

Now, we have entered the "internet plus" era, and all industries are making use of information technology to seek a longer-term development. For vegetable distribution, we must rely on the power of information technology. Faced with complex global optimization problems, traditional optimization algorithms have many performance limitations, such as complex solution process and easy to fall into local optimization, which can't meet the needs of practical applications. This paper studies the design and application of optimization algorithm for vegetable planting and distribution based on adaptive differential evolution algorithm. The method adopted in this paper is adaptive differential evolution algorithm. In the original differential evolution algorithm, there are three undetermined parameters: population size (NP), variation factor (F) and crossover probability (CR). In the process of evolution, NP takes a fixed value, while the key control parameters F and CR, which affect the performance of the algorithm, will adaptively take different values on [0,1], and the values of F and CR will be recalculated for each individual in each generation. Let F and CR automatically adjust and change in the process of evolution. The research in this paper shows that the algorithm in this paper is effective and suitable for application. [ABSTRACT FROM AUTHOR]

• A robust method is proposed to alleviate the effect of traffic data with outliers. • A comprehensive traffic flow prediction indicator system is established in this paper. • We hybridize polynomial and Gaussian kernel to build nonlinear version of our method. • Parameters of the proposed method are optimized by Fruit Fly Optimization Algorithm. A novel least squares twin support vector regression method is proposed based on the robust L 1 -norm distance to alleviate the negative effect of traffic data with outliers. Although there is some known work for the short-term traffic flow prediction problems, their efficacy depends heavily on the collected traffic data, which are often affected by various external factors (e.g. weather, traffic jam or accident), leading to errors and missing data. This makes it difficult to pick an effective method that accurately predicts the traffic state. As a contribution of this paper, an iterative algorithm is designed to solve the non-smooth L 1 -norm terms of our method; its convergence also proved. Further, a comprehensive traffic flow indicator system based on speed, traffic flow, occupancy and ample degree is utilized in this paper. We also extend the proposed method to a nonlinear version by hybridizing the polynomial kernel and radial basis function kernel, where the weight coefficient of hybrid kernel is determined by the change tendency of traffic data. To promote the prediction performance, the parameters of our nonlinear method are optimized by adaptive fruit fly optimization algorithm. Extensive experiments on real traffic data are performed to evaluate our model. The results indicate that the newly constructed model yields better prediction performance and robustness than other models in various experimental settings. [ABSTRACT FROM AUTHOR]

Firefly algorithm (FA) is a meta-heuristic optimization algorithm inspired by nature. Due to its superior performance, it has been widely used in real life. However, it also has some shortcomings in some optimization cases, such as low solution accuracy and slow solution speed. Therefore, in this paper, distributed parallel firefly algorithm (DPFA) with four communication strategies is presented to improve these shortcomings. The distributed parallel technique is implanted to divide the initial fireflies into several subgroups, and exchange the information based on communication strategies among subgroups after the fixed iteration. The communication strategies include the maximum of the same group, the average of the same group, the maximum of different groups and the average of different groups. For verifying its performance, this paper compared DPFA with famous optimization algorithms, and experimental results show that DPFA has stronger competitiveness under the test suite of CEC2013. Furthermore, the proposed DPFA is also applied to the PID parameter tuning of variable pitch wind turbine, and conducted experiments show that DPFA outperforms other algorithms. It can smooth the power output and reduce the impact on the power grid when the wind speed fluctuates. • Developed distributed parallel mechanism as a method to balances the exploration and exploitation of algorithms. • A proposed distributed parallel firefly algorithm (DPFA) improves the performance of the original FA. • Compared DPFA with other well-known optimization algorithms under the test suite of CEC2013. • DPFA is applied in the control of variable pitch wind turbine to tune PID parameters. [ABSTRACT FROM AUTHOR]

Particle Swarm Optimization (PSO) provides a metaheuristic optimization technique for real-life functions with complex behavior. Inspired by the metaphor of social behavior as exhibited by swarms operating in natural surroundings, the algorithm captures the Emergent Complexity demonstrated by individual components of large groups which work together using simple rules and create complex, yet efficient, adaptive, and self-organizing systems. The paper seeks to rigorously analyze the hyperparameters of the algorithm to find the relative importance of cognitive(personal) vis-à-vis social (swarm) parameters of the algorithm so as to gain a perfect understanding of the importance of exploitation versus exploration parameters thus enabling us to enhance the application efficiency of the algorithm. The paper further suggests and applies a rolling coefficient updating technique to the algorithm parameters so as to use a formal statistical procedure in the initialization and subsequent iterations for updating coefficients rather than leaving it on trial and error or on a practitioner's knowledge. Results suggest that the technique employed provides an enhancement in the efficiency of the algorithm by creating a dynamic Emergent Complexity environment. [ABSTRACT FROM AUTHOR]

In this paper feasibility-preserving genetic operators for hybrid algorithms using TSP solvers for the Inventory Routing Problem (IRP) are studied. The IRP is a problem of jointly optimizing delivery schedules and routes for vehicles transporting products from a supplier to a number of retailers. This optimization problem is highly constrained, because limits on inventory levels as well as the vehicle capacity have to be taken into account. Moreover, the IRP is a generalization of the TSP and solving the TSP effectively is an important part of obtaining good solutions to the IRP. In this paper evolutionary algorithms are used for solving the IRP, but finding good routes is delegated to a state-of-the-art TSP solver. Therefore, genetic operators used in this paper focus on constructing the delivery schedule and not on optimizing the routes. In the experimental part of the paper an evolutionary algorithm using feasibility-preserving genetic operators is compared to the Infeasibility Driven Evolutionary Algorithm (IDEA) that uses the selective pressure to obtain feasible solutions. Presented results suggest that designing good feasibility-preserving genetic operators is important, because allowing the optimization algorithm to generate infeasible solutions and handling infeasibility in IDEA using the selective pressure leads to inferior results. [ABSTRACT FROM AUTHOR]

This research paper proposes a hybrid Whale Optimization Algorithm (WOA) variant based on Equilibrium Optimizer (EO), named Equilibrium Whale Optimization Algorithm (EWOA). The major finding lies in an efficient hybridization of bio-inspired (WOA) and physics-based (EO) metaheuristic algorithms. Upon mathematical modelling, EWOA proposes a main architecture that combines WOA's encircling and net-bubble attacking mechanisms via EO's weight balance strategy. The proposed algorithm was tested on 23 classical, 28 constrained CEC 2017, 30 unconstrained CEC 2017, 10 CEC 2019, and 10 CEC 2020 benchmark problems, in comparison with six recently proposed state-of-the-art algorithms (including WOA and EO). EWOA outperforms other algorithms with the best statistical mean performance on 46 out of 101 functions and the most promising clustering data in the graph, respectively. The fact that EWOA could achieve best statistical SD performance on 2 of the total 5 benchmark sets proves that EWOA is competitively robust. EWOA can converge to the optimum before 50% iterations of most benchmark functions, achieving the fastest convergence rate compared to other algorithms. The major contribution thereby lies in the successful development of this hybrid algorithm, which yields better optimization efficiency than the original and other state-of-the-art algorithms in terms of statistics, convergence and clustering. [ABSTRACT FROM AUTHOR]

Rule-based classification is one of the important tasks in data mining due to its wide applications, particularly in the domains that need to interpret the classification decision such as medical diagnosis. The rule-based classification is a combination of the classification and association rule mining fields which aims at building interpretable classifiers by means of classification rules. This paper presents a novel and efficient sequential covering strategy for Classification Rule Mining to improve the interpretability of classifiers using a Discrete Equilibrium Optimization Algorithm called DEOA-CRM. Our approach benefits from the advantages of associative classification and population-based intelligence. It is inspired by the recent meta-heuristic equilibrium optimization algorithm. New discrete operators defined enable our approach to avoid local solutions and find global ones, improving the exploration and exploitation power in the search space. The proposed DEOA-CRM is tested on a total of 12 test data sets of various sizes and benchmarked with four recent and well-known rule-based classification mining algorithms. The obtained results confirm the efficiency of our algorithm in three chosen measures. Our approach fully deserves its use for classification rules generation to help decision-makers generate accurate and interpretable models. [ABSTRACT FROM AUTHOR]

Decision tree induction is a simple, however powerful learning and classification tool to discover knowledge from the database. The volume of data in databases is growing to quite large sizes, both in the number of attributes and instances. Some important limitations of decision trees are instability, local decisions, and overfitting for this extensive data. The simple, effective and non-convergence nature of the African Buffalo Optimization (ABO) algorithm makes it suitable to solve complex optimization problems. In this paper, we propose the African Buffalo Optimized Decision Tree (ABODT) algorithm to create globally optimized decision trees using the intelligent and collective behaviour of African Buffalos. The modified African Buffalo optimization algorithm is used to create efficient and optimal decision trees. To evaluate the efficiency of the proposed African Buffalo Optimized Decision Tree algorithm, experiments are performed on 15 standard UCI learning repository datasets that are of various sizes and domains. Results show that the African Buffalo Optimized Decision Tree algorithm globally optimizes decision trees, increases accuracy and reduces the size of a decision tree. These optimized trees are stable and efficient than conventional decision trees. [ABSTRACT FROM AUTHOR]

K-anonymity is the most widely used approach to privacy preserving microdata which is mainly based on generalization. Although generalization-based k-anonymity approaches can achieve the privacy protection objective, they suffer from information loss. Clustering-based approaches have been successfully adapted for k-anonymization as they enhance the data quality, however, the computational complexity of finding an optimal solution has shown as NP-hard. Nature-inspired optimization algorithms are effective in finding solutions to complex problems. We propose, in this paper, a novel algorithm based on a simple nature-inspired metaheuristic called Black Hole Algorithm (BHA), to address such limitations. Experiments on real data set show that data utility has been improved by our approach compared to k-anonymity, BHA-based k-anonymity and clustering-based k-anonymity approaches. [ABSTRACT FROM AUTHOR]

With the rapid development of wireless communication technology, new wireless applications and services are exploding. These new applications and services usually need wider bandwidth and higher speed. The voice of people calling for a fast and convenient network is getting stronger and stronger, and the wireless network must be the main development direction of the network in the future world. In order to make the development of wireless communication technology not limited by limited resources, resource optimization design has always been a research hotspot of scientists. Among them, the optimal resource algorithm can get the theoretical global optimal solution, while the sub-optimal resource allocation algorithm, as a supplement to the former, can be used to reduce costs and improve efficiency when the system structure is too complex. In this paper, a resource optimization method based on sparse clustering algorithm is proposed, and the matching relationship between spectrum resources and the services carried by cognitive terminals is established, so that the effectiveness of collaboration and cognitive systems is verified. [ABSTRACT FROM AUTHOR]

The meta-heuristic algorithms have aroused great attention for controller optimization. However, most of them are inseparable from the explicit system models when addressing a constrained optimization problem (COP). In this paper, we propose a data-driven constrained bat algorithm via a gradient-based depth-first search (GDFS) strategy. In the proposed scheme, the GDFS strategy can predetermine a search space that satisfies some strict constraints (e.g., stability requirements) of the optimized system. Meanwhile, an improved boundary constraint handling method is proposed to limit the exploration process to the predetermined space. In this way, the proposed algorithm can solve the COP by utilizing experimental data from real scenes, thereby relieving the dependence on precisely modeling the complex system. Together with an ɛ -constraint-handling method, the bat algorithm is employed to seek the global optimum of the COP. The search performance is enhanced by the designed linear-varying elite layer-based local search and a social learning-based walk mechanism to dynamically balance exploration and exploitation. The convergence is ensured based on the criteria of the stochastic optimization algorithm. Experimental results on a servo drive system and benchmark test functions verify the effectiveness of the proposed algorithm. • Design a data-driven optimization method for the constrained controller tuning. • Design a gradient-based depth-first search strategy for strict constraints. • BA integrates a novel search mechanism to prevent premature convergence. • An improved boundary constraint handling scheme is proposed. [ABSTRACT FROM AUTHOR]

An accurate and efficient control of the Maximum Power Point Tracking (MPPT) and pitch angle for the Wind Turbine (WT) system can provide more energy while also protecting the material. Thus, considering these economic aspects, many controllers are proposed to guarantees their performances. The most studied strategy is the classical PID controller. However, its performance is limited due to the nonlinear model and the difficult wind speed form, which inflict many uncertainties. To solve these drawbacks, an advanced controller named Type-2 Fractional Order Fuzzy PID (T2-FOPID) controller is suggested in this paper to improve the exigence controls. Meanwhile, an efficient optimization technique named the Salps Swarm Algorithm (SSA) has been introduced in order obtaining the best controller parameters. The efficiency of the proposed controller is tested to the 10 kW-WT under various wind speed scenarios. The derived results explicitly indicate that the proposed strategy outperforms the PID controller and other controllers, in terms. the minimum of error, the overshoot and the settling time. • The pricipal controls of the wind turbine system are are the MPPT and the pitch angle. • The authers proposed the Type-2 Fractional order PID controller based on salps swarm algorithm. • The results proved the superiority of proposed controller than the classical PID controller and others are largely suggested in the literature. [ABSTRACT FROM AUTHOR]

Classification is used to categorize data and produce decisions for several domains. To improve the accuracy of classification, researchers have tended to hybridize the neural network with other metaheuristic algorithms in order to better exploit and explore the search space and thereby solve many different classification problems in an effective manner. The hybridization of algorithms is now commonplace and has resulted in the creation of novel methods that are more effective in comparison with those that employ a sole algorithm. Therefore, in this paper, a hybridization approach is employed to utilize the African buffalo optimization (ABO) algorithm as an optimizer to adjust the weights of the probabilistic neural network (PNN). The effectiveness of the proposed (ABO-PNN) method is investigated by applying it to several different classification problems. The efficiency of the ABO algorithm is assessed based on the PNN training results produced and its performance is compared with that of different types of optimization algorithm. The performance of the proposed algorithm in terms of classification accuracy is tested on 11 benchmark datasets. The results show that the ABO is better than the firefly algorithm (FA) in terms of both classification accuracy and convergence speed. [ABSTRACT FROM AUTHOR]

Email spam has been a big issue in recent years. As the percentage of internet users grows, so does the number of spam emails. Technologies are being used for illegitimate and immoral activities, such as phishing and robbery. As a consequence, it is essential to identify fraudulent spammers by employing machine learning techniques. This paper presents a novel spam classification technique that integrates the Harris Hawks optimizer (HHO) algorithm with the k-Nearest Neighbors algorithm (k-NN). The Harris Hawks Optimization (HHO) algorithm is a new metaheuristic algorithm motivated by Harris' Hawks' cooperative relations and surprises pounce pursue technique in nature. According to empirical results on the dataset, the suggested model can handle high-dimensional data (Spambase). The suggested model's spam detection accuracy is compared to the numerous algorithms, including the Binary Dragonfly Algorithm (BDA), Equilibrium Optimizer (EO), Teaching-Learning-based Optimization, Seagull Optimization Algorithm (SO), and Marine Predators Algorithm (MPA). We found that the proposed technique trumps the other spam detection techniques investigated in this study in terms of classification accuracy. The experimental results showed that the proposed technique accuracy reaches %94.3. [ABSTRACT FROM AUTHOR]

The network structure exhibits a variety of changes over time. Fusing this structure and the development of communities in dynamic networks plays an important role in analyzing the evolution and development of the entire network. How to ensure the division of the community structure in social network big data, as well as ensure the continuity of the community between the current time and previous time period, are issues that need to be explored. This problem can be solved by fusing the three characteristics of temporal variability, stability, and continuity in dynamic social network communities, and by adopting the multi-objective optimization method to detect community structures in dynamic networks. The probability fusion method is added to the initial step of the algorithm to generate suitable network partitions and ensure fast convergence and high accuracy. Two neighboring fusion strategies are proposed that are suitable for communities: the neighbor diversity strategy and the neighbor crowd strategy. These two strategies make different changes to the candidate network partitions. A continuity metric for dynamic community evolution is formulated to compare the similarity of the dynamic network communities of two consecutive time steps. Experiments on synthetic datasets and actual datasets prove that the proposed method in this paper provides better performance than existing methods. [ABSTRACT FROM AUTHOR]

In this paper, the authors analyze the algorithm optimization and anomaly detection simulation based on extended jarvis-patrick clustering and outlier detection. We perform detection by using the jarvis-patrick graph-based clustering method. After that, to further improve the false alarm rate (FAR) of the algorithm, we use an extra outlier detection step combined with our proposed EJP to create a new anomaly detection method called LD-EJP. Using LD-EJP, the false alarm rate improved much (experiments show that the false alarm rate can reach 4.1% while the best JP clustering can reach is 7.4%). Then, we tested LD-EJP against two other anomaly detection methods using k-means and LGCCB, showing that our algorithm has a better detection rate and false alarm rate than these two clustering-based anomaly detection methods. In addition, the detection rate and false positives of the algorithm also have some room for improvement. In the labeling process, the proportion of anomaly clusters to normal clusters needs to be manually adjusted to find a better detection rate. In addition, the detection rate we chose can consume some of the detection rate gained in extended JP clustering to have the LD-EJP obtain a better FAR. Therefore, our future work contains finding or proposing another outlier detection algorithm with better performance than our LD-EJP method. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hybrid algorithm which is mathematically modelled by amalgamating the superior features of recently developed Grey Wolf Optimizer (GWO), Sine Cosine Algorithm (SCA), and Crow Search Algorithm (CSA). Researchers have already implemented the aforementioned three algorithms and obtained superior quality results for solving diverse optimization problems. The novel hybrid Variegated GWO Algorithm (VGWO) developed in this proposed research work is initially realized and validated for solving IEEE CEC-C06 2019 benchmark functions. Thereafter, the proposed VGWO is utilized as an optimization tool to solve three emerging and complex power system optimization problems which includes energy management of microgrid systems operated in both islanded and grid-connected mode, dynamic economic emission dispatch and reactive power planning (RPP) problem. A comparative analysis of the proposed VGWO approach with other established metaheuristics is undertaken for each optimization problem. Numerical results show that the novel hybrid VGWO algorithm outperformed an ample number of optimization techniques in providing better quality solutions. The proposed hybrid algorithm yielded a 36.93% reduction in active power loss and 36.80% reduction in operating cost with respect to base case condition for RPP problem. Likewise while solving microgrid energy management problems 9–30% savings was realized in the generation cost compared to the ones mentioned in literature. The capability of handling many complex constraints within a minimum amount of computational time to provide consistently best solutions prioritize the proposed hybrid algorithm among its kinds. Statistical analysis validates the authenticity and viability of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Particle Swarm (PSO) and Bacterial Foraging (BF) Optimizers are two widely used optimization techniques. A proper combination of these two algorithms would improve their search capability while minimizing their shortcomings, such as parameter dependency and premature convergence. This paper presents a hybrid optimization algorithm that combines PSO and BF (HPSBF) to ensure security and the system's stability following faults and disturbances. The formulated objective function is claimed to be innovative and straightforward. The set objectives are to minimize the dropped load by shedding relays while maximizing the lowermost swing frequency. The optimal operation of Under-Frequency Load-Shedding (UFLS) Relays is driven by the HPSBF technique as a bounded optimization with bounds representing the limits of the system's state variables. The viability of the HPSBF is verified against conventional-, PSO-, and BF-UFLS approaches. The standard IEEE 9-bus and IEEE 39-bus systems are exploited to examine the response of the developed UFLS techniques. The tested systems are exposed to various operational scenarios such as loss of power plants and a considerable abrupt load increase. The DigSilent power factor software is used to simulate the IEEE 9- and 39-bus systems, while MATLAB code was implemented to obtain optimal operational points for the implemented algorithms. The HPSBF accomplished the uppermost swing frequency and the lowermost quantity of the disconnected load. Furthermore, the computational times of HPSBF are equivalent to those of the PSO. [ABSTRACT FROM AUTHOR]

In the cloud computing environment, scheduling algorithms show the vital role of finding a possible schedule of the tasks. Extant literatures have shown that the task scheduling problem is NP-Complete as the objective is to obtain the minimum overall execution time. In this paper, we address the problem of scheduling a set of l tasks with a set of | G | groups to a set of m clouds, such that the overall layover time is minimized. Note that overall layover time is the sum of the timing gaps between paired tasks. Here, we present a pair-based task scheduling algorithm for cloud computing environment, which is based on the well-known optimization algorithm, called Hungarian algorithm. The proposed algorithm considers an unequal number of tasks and clouds, and pairs the tasks to make the scheduling decision. We simulate the proposed algorithm and compare it with three existing algorithms, first-come-first-served, Hungarian algorithm with lease time and Hungarian algorithm with converse lease time in twenty-two different datasets. The performance evaluation shows that the proposed algorithm produces better layover time in comparison to existing algorithms. The proposed algorithm is analyzed theoretically and shown to require O (kpl 2) time for k iterations, p repetitions and l tasks. [ABSTRACT FROM AUTHOR]

In the electrical power system optimal power flow is a solution for power management with multi variable equality and inequality constraints. Different algorithms were used to solve the optimum power flow (OPF). This paper proposes the optimal power flow using efficient and reliable optimization technique of enhanced sine cosine algorithm (ESCA). The proposed algorithm used to solve the multi objective power flow function problems such as generating cost, losses, emission of power plant and to improve the voltage stability. In this IEEE standard 30 bus system with four different multi objective functions have been tested and results obtained by Enhanced sine cosine algorithm are compared with other evolutionary algorithms. The results show that ESCA gives better results than other techniques in terms of convergence speed and global optimal solutions. [ABSTRACT FROM AUTHOR]

Multilinear model predictive control (MLMPC) can regulate a nonlinear process with wide operating regions based on a set of linear models. Although online computational cost is reduced compare to nonlinear MPC (NMPC), it is difficult to obtain a reliable full nonlinear model or set of linear models in practice. In this paper, we propose a combination of MLMPC with differential dynamic programming (DDP), so that the system can be controlled offset-free in the absence of a full nonlinear model. DDP is a 'trajectory-centric' optimization technique that solves nonlinear optimal control problems. The trajectory can be optimized even if the full model for the system is unknown, because DDP uses only the gradients around the visited trajectory, which is easily obtained by input excitations. Moreover, the gradient information can provide linear models in the subsequent MLMPC step. In the proposed scheme, a novel model selection based on gap metric and weighting method are employed for MLMPC. We prove the offset tracking property of DDP assisted MLMPC. A continuous stirred tank reactor (CSTR) process is studied to demonstrate the effectiveness of the proposed algorithms. Simulation studies show that CDDP designed by the proposed algorithm improves the trajectory over iterations, and the resulting MLMPC achieves offset-free tracking property regardless of an initial point and a set-point in the operating region. • Data-driven DDP is proposed to find the optimal trajectory without models of nonlinear systems. • LTV model is obtained from Data-driven DDP and exploited for the model of MLMPC. • A novel clustering method based on gap metric is proposed to choose linear models for MLMPC. • MLMPC with a novel prediction-based weighting method is proposed and its stability is discussed. • The proposed MLMPC strategy achieves offset-free control over large operating ranges. [ABSTRACT FROM AUTHOR]

It is a common occurrence nowadays for a network to be gigantic in size and complex in architecture. Network navigation thus faces many new problems in terms of routing and resource utilization, one of which arises in multi-domain networks. This paper focuses on the Inter-Domain Path Computation under Node-defined Domain Uniqueness Constraint (IDPC-NDU) problem, whose purpose is to find the shortest path between two nodes in a network under a constraint that such a path is only allowed to traverse each domain at most once. Considering that this is an NP-Hard problem, an approximate approach is more practical than an exact one. Meanwhile, Particles Swarm Optimization Algorithm (PSO) has been long known for its powerful ability to discover near-optimal solutions in a reasonable time; however, its application in discrete search space is limited. Therefore, we decide to use the multi-population framework, a sub-branch of multitasking optimization which allows information exchange not only between problems but also between different optimization heuristics, to improve upon the basic PSO method. Specifically, this paper introduces a hybridization between PSO and Variable Neighborhood Search (VNS). The encoding and decoding method are created specifically for the IDPC-NDU problem and for the PSO algorithm, and VNS serves to enhance further the algorithm's ability to escape local optima. Experiments are carried out to prove the new algorithm's efficacy, especially in the context of similar multitasking methods. • Propose a multi-tasking technique for IDPC-NDU. • Propose novel encoding and decoding methods for the IDPC-NDU problem. • Integrate PSO and VNS into a multi-population-based approach to solve the IDPC-NDU problem. • Evaluate the efficiency of the proposed algorithm on various instances. [ABSTRACT FROM AUTHOR]

• This paper proposes an enhanced SCA algorithm variant, called ARSCA. • ARSCA improves the convergence accuracy and avoids premature convergence. • This paper compares ARSCA with classical algorithms, variations of other algorithms. • ARSCA is applied to six engineering design problems and its results are satisfactory. The sine cosine algorithm (SCA) is a well-known meta-heuristic optimization algorithm. SCA has received much attention in various optimization fields due to its simple structure and excellent optimization capabilities. However, the dimension of objective function also increases with the increasing complexity of optimization tasks. This makes the original SCA appear to have insufficient optimization capability and likely to fall into premature convergence. A multi-mechanism acting variant of SCA, called ARSCA, is proposed to address the above deficiencies. ARSCA is an enhanced SCA algorithm based on the adaptive quadratic interpolation mechanism (AQIM) and Rounding mechanism (RM). RM enables a more balanced state between exploration and exploitation of the ARSCA. AQIM enhances local exploitation capabilities. To verify the performance of ARSCA, we compared ARSCA with some advanced traditional optimization algorithms and variants of algorithms for 30 consecutive benchmark functions of IEEE CEC2014. In addition, ARSCA was applied to 6 constrained engineering optimization problems. These six algorithms include the tension–compression spring design problem, the welded beam design problem, the pressure vessel design problem, the I-beam design problem, the speed reducer design problem, and the three-bar design problem. Experimental results show that ARSCA outperforms its competitors in both the solution quality and the ability to jump out of the local optimum. The relevant codes for the paper are publicly available at https://github.com/YangXiao9799/paper_ARSCA. [ABSTRACT FROM AUTHOR]

In this paper, a hybrid algorithm was proposed for multi-objective optimization design with high efficiency and low computational cost based on the Gaussian process regression and particle swarm optimization algorithm. For the proposed method, the global performance indices, including regular workspace volume, global transmission index, global stiffness index, and global dynamic index were considered as objective functions. First, the multi-objective optimization problem considering the boundary conditions, objective, and constraint functions was constructed. Second, the Latin hypercube design was regarded as the design of experiment to obtain the computer sample points. Besides, the high-precision objective-function values were obtained by increasing the node density in the workspace at these sample points to provide sufficient information for the mapping model. Third, the Gaussian process regression was proposed to build the mapping model between the objective functions and the design parameters, thus reducing the computational cost of global performance indices. Cross-validation and external validation were adopted to verify the mapping model. Finally, the hybrid algorithm combined with the Gaussian process regression and particle swarm optimization algorithm was proposed for multi-objective optimization design. The 2PRU-UPR parallel manipulator was taken as a case to implement the proposed method (where P was a prismatic joint; R a revolute joint; U a universal joint). The comparison from the back propagation neural network, multivariate regression, and Gaussian process regression mapping models showed that the Gaussian process regression model had higher accuracy and robustness. The proposed hybrid algorithm saved 99.84% of computational cost compared to using the particle swarm optimization algorithm. The Pareto frontier of the multi-objective optimization problem of the 2PRU-UPR parallel manipulator was also obtained. After optimization , the performance indices were significantly improved. • A mapping modeling between objective functions and design parameters based on Gaussian process regression is proposed. • A multi-objective optimization design method combined with the mapping model and particle swarm algorithm is proposed. • The proposed method can significantly reduce the computational cost for the high computation-intensive design problems. • A study on the comparison of the robustness and accuracy among the three mapping models is carried out. • The three mapping models are multivariate regression, BP neural network, and Gaussian process regression models. [ABSTRACT FROM AUTHOR]

This paper proposes a novel robust supervised subspace learning (RSSL) method for output-relevant prediction and detection against outliers. RSSL learns the robust subspaces by optimizing a joint problem over both the prediction of output and the reconstruction of input. To this end, the learned subspaces/data representations are informative, i.e., they are encapsulated with the critic information related to both the input and output, and thus can benefit the following tasks of output-related modeling and detection. Besides, we separate sparse items from the raw measurements to suppress the effects of outliers. An efficient optimization algorithm is designed to solve the optimization problem of RSSL. We further conduct post orthogonal decomposition upon the subspaces provided by RSSL so that the trimmed subspaces are more suitable for output-related detection. The efficacy of the proposed method is extensively verified by synthesis data and benchmark data. • We learn robust latent subspaces by a joint optimization of input and output, where the negative effects of outliers are suppressed by separating sparse items from the original data. • We develop an efficient algorithm to solve the proposed formulation of RSSL. The theoretical and empirical analysis demonstrate the effectiveness of the designed optimization algorithm. • The proposed RSSL is competent to the tasks of output prediction and output-related detection, where, for the latter task, post-decomposition is performed to further trim the subspaces generated by RSSL. [ABSTRACT FROM AUTHOR]

Coal consumption and free calcium oxide (f-CaO) content are two important production indicators in the cement calcination process, and its collaborative optimization is of great significance to improve the production performance. However, due to the multiple dynamic interferences and multiple conflicts between coal consumption and f-CaO, product quality is unstable and coal consumption becomes high. To address the problems, this paper proposes a multi-objective collaborative optimization method for cement calcination process. This method takes the coal consumption and f-CaO content as the optimization objectives to establish a multi-objective optimization model. In addition, to solve the problem that static single-step optimization is difficult to track the continuous change of working conditions, we propose a time domain rolling multi-objective Jaya algorithm (TDRM-Jaya) to optimize the model. The algorithm, involving a population update strategy, a selection strategy and a time domain rolling strategy, realizes the dynamic rolling optimization for the cement calcination process. The test comparison with several common intelligent optimization algorithms demonstrates the advantages of using TDRM-Jaya. Besides, practical industrial data validates the effectiveness of the proposed optimization method. [Display omitted] • A multi-objective optimization model of cement calcination process is developed. • A TDRM-Jaya algorithm is proposed applied to solve the optimization model. • The multi-objective collaborative optimization using data-driven model is studied. • Computational results show that the expected optimization effect can be achieved. [ABSTRACT FROM AUTHOR]

within constraint satisfaction problems (CSPs), in this paper we introduce a honeybee algorithm that uses new template concept for image segmentation. Our approach uses the guided local search and the graph theory to achieve an improved image segmentation. Compared to previous well known heuristics, our approach is giving better experimental results. [ABSTRACT FROM AUTHOR]

• The significant and impressive performance of the Chimp Optimization Algorithm (ChOA) has been a great inspiration for this paper to study on its multi-objective version called MOChOA. • A memory structure has been exploited as an archive to store the non-dominated solutions alongside a leader selection strategy and grid mechanism. The leader selection procedure consists of analyzing the archive contents to choose the best candidates for the leader of independent groups, which are drivers, barriers, chasers, and attackers. The strategy also guarantees the exploration of search space. • Twelve test problems with various shapes and different dimensions have opted to evaluate MOChOA. The results of coverage, generational distance, averaged Hausdorff distance, spacing, diversity, and other metrics are considered in the performance evaluation of the algorithm. The Multi-Objective Chimp Optimization Algorithm (MOChOA), a multi-objective variation of the recently proposed ChOA, is developed in this research to address multi-objective optimization issues in various engineering problems. The optimization community has recently been offered numerous evolutionary and meta -heuristic optimization strategies for tackling optimization challenges. When analyzing multi-objective optimization (MOO) problems, these approaches frequently produce poor solutions because they do not accurately estimate the Pareto optimal solutions or increase the distribution across all objectives. The significant and impressive performance of the ChOA has been a great inspiration for this paper to study on its multi-objective version called MOChOA. In this approach, a memory structure has been exploited as an archive to store the non-dominated solutions alongside a leader selection strategy and grid mechanism. The leader selection procedure consists of analyzing the archive contents to choose the best candidates for the leader of independent groups, which are drivers, barriers, chasers, and attackers. The strategy also guarantees the exploration of search space. Twelve test problems with various shapes and different dimensions have opted to evaluate MOChOA. The results of coverage, generational distance, averaged Hausdorff distance, spacing, diversity, and other metrics are considered in the performance evaluation of the algorithm. According to results, MOChOA can provide competitive results and outperform well-known intelligent algorithms such as ANSGAIII, ARMOEA, dMOPSO, EFRRR, IBEA, SPEA2SDE, SPEAR, and tDEA. Alongside the remarkable performance of the proposed algorithm, its light-weight structure causes a fast execution. The proposed MOChOA can be applied in various multi-objective optimization application, including engineering, science, chemical processes, economics and logistics when appropriate decisions must be made in the face of competing objectives. [ABSTRACT FROM AUTHOR]

• This paper develops a data aggregation technique using optimization model for WSN-based IoT network. The overall procedure involves simulation of WSN-based IoT, multipath routing and finally data aggregation. • At first, the WSN-based IoT nodes are simulated, which are subjected to multipath routing process. The multipath routing is done using DEMFO, which is devised by combining Dolphin Echolation (DE) and Moth fly optimization technique (MFO). From the obtained paths, the data routing is performed wherein data are subjected to base station. • In base station, the data aggregation is done using proposed Hierarchical autoregressive Bidirectional Least-Mean-Square algorithm (HABLMS). The proposed HABLMS is devised by combining Hierarchical autoregressive (HA) using Caviar model and Bidirectional Least-Mean-Square algorithm (BLMS). Thus, the weights are updated in an optimal manner. • Thus, the proposed HABLMS algorithm helps to adapt optimal aggregation of data in WSN-based IoT network. The Internet of Things (IoT) is emerging that acquired the interest of several researchers, as it inspires each object to offer services and data to users by collaborating with each other. The Wireless sensor network (WSN) is a major constituent of IoT in transmitting data. Various wireless sensor-based protocols are developed for path selection. This paper develops a data aggregation technique using an optimization model for WSN-based IoT network. The overall procedure involves simulation of WSN-based IoT, multipath routing and finally, data aggregation. At first, the WSN-based IoT nodes are simulated, which are subjected to a multipath routing process. The multipath routing is done using DEMFO, which is devised by combining Dolphin Echolation (DE) and Moth fly optimization technique (MFO). From the obtained paths, the data routing is performed wherein data are subjected to the base station. In the base station, the data aggregation is done using the proposed Hierarchical autoregressive Bidirectional Least-Mean-Square algorithm (HABLMS). The proposed HABLMS is devised by combining Hierarchical autoregressive (HA) using the Caviar model and Bidirectional Least-Mean-Square algorithm (BLMS). Thus, the weights are updated in an optimal manner. Thus, the proposed HABLMS algorithm helps to adapt optimal aggregation of data in a WSN-based IoT network. The developed DECSA+HABLMS offered superior performance with minimal prediction error of 0.0147. [ABSTRACT FROM AUTHOR]

Optimal coordination and settings of directional overcurrent relays (DOCRs) are crucial task to ensure reliable and effective protection in power system networks. The settings of DOCRs are plug setting current (PSC) and time multiplier setting (TMS). The DOCRs coordination is well-known as a highly constrained nonlinear optimization problem. Its complexity in terms of non-linearity increases along with the network size increment. This paper proposes a hybrid optimization algorithm which consists of Firefly Algorithm and Linear Programming (FA-LP) to relax the search space by linearizing the equation of the DOCRs coordination to attain an optimal solution. Furthermore, this paper also considers a mixed type of IEC relay characteristics to attain effective relay operation. The proposed method is tested on the IEEE 8-, 15- and 30-bus test systems. The results show the reduction of total relay operating time between 15.6% and 85.5% as compared to other techniques in the literature and also being verified using the ETAP software. [ABSTRACT FROM AUTHOR]

Moving sensor nodes can mitigate the coverage problem of random deployment in wireless sensor networks. However, the movement of nodes affects the lifetime and integrity of the network. Therefore, both energy saving and efficient coverage are crucial factors. In this paper, we propose an energy-efficient coverage optimization technique with the help of the multi-Strategy grey wolf optimization (MSGWO) algorithm. This method can reduce energy consumption and improve coverage area by mixing higher-order multinomial sensing models and a sort-driven hybrid opposition-based learning. In addition, node movement and boundary strategies are proposed to help nodes jump out of obstacles when facing obstacle-aware deployments. The MSGWO is validated and compared on several classical test functions, and the results show that the MSGWO performs well. The MSGWO algorithm is applied to optimize the WSN coverage on different obstacle scenarios, the experimental results show that the algorithm helps to increase the network coverage from 84 % to 97.86 %, extends the network lifecycle by 50 %, reduces the cost of node deployment, and the network has good connectivity and scalability. [ABSTRACT FROM AUTHOR]

The optimisation of the corridor allocation problem (CAP) belongs to the optimisation of the efficiency of the automated production line. The goal is to reduce the material handling cost (MHC) in the production process through a reasonable layout of the facilities, so as to save expenses for the enterprise. In recent years, with the acceleration of market changes, product design and production process adjustments have become more frequent, and more attention has been paid to the research on the layout of facilities under the condition of changes in the flow of materials between production facilities over time. On the basis of the CAP model, this paper considers the optimisation problem of row layout when the flow of materials between facilities fluctuates in a certain range. The new model can be utilised to obtain the overall optimisation solution under the condition of the floating material flow matrix, so as to achieve the goal of optimising the total MHC in the entire production process. As the new model introduces more variables and intermediate parameters, a two-stage solution method is previously required, which greatly increases the time to solve the problem. This paper proposes a targeted meta-heuristic algorithm optimisation method combining the advantages of tabu search algorithm and harmony search algorithm, which simplifies the solution phase of calling the precise solver in the two-stage algorithm of row facility layout problem, improves the problem solving efficiency, and makes the solution of large-scale problems become possible. The proposed model is verified through Lingo software, and then the model and the hybrid algorithm in the MATLAB environment are verified with each other. Finally, the proposed simplified algorithm is utilised to solve the large-scale problems that could not be solved by the two-stage algorithm before. [ABSTRACT FROM AUTHOR]

This paper develops a robust strategy based on integrating three mutation phases and adapted control parameters into the Adaptive Guided Differential Evolution algorithm called (mAGDE) to improve diversity and exploration of the original AGDE. The mAGDE performance is evaluated using IEEE CEC'2020 test suite. Furthermore, the mAGDE is employed to identify the solid oxide fuel cell (SOFC) model optimal parameters. Two modes of SOFC operation are investigated, steady and transient states. The results obtained from the proposed mAGDE are compared with a number of recent, well-established and reputed meta-heuristics, including Particle swarm optimization, Teaching learning-based optimization, Whale optimization algorithm, Harris hawks optimization, Marine predators algorithm, Archimedes optimization algorithm, Differential evolution, and the original AGDE. Additionally, the statistical parameters that measure the performance of the proposed optimizer and the other competitors are calculated. The main finding demonstrated the preference and robustness of the suggested mAGDE in constructing the SOFC circuit that closely converges to the actual one. During the steady-state operation, the best fitness value obtained via the suggested mAGDE for operation at 1273 K is 2.2995E−06, while in the transient-state operation, the best SMSE is 1.04. The average cost function is decreased by 43.33% compared to the one obtained by the original AGDE. From the aforementioned assessments, it can be concluded that the proposed mAGDE is outstanding and promising. • This paper introduces a modified mAGDE based on the integration of a modified three-phases AGDE with the DE_SPA algorithm. • A modified three-phases AGDE has been introduced to maintain diversity and convergence of the evolutionary process. • The CEC'2020 test suite problems are used to demonstrate the performance mAGDE against other competitors. • Applying for first time mAGDE to identify the optimal parameters of SOFC model. • The robustness of the proposed strategy-based mAGDE is proved. [ABSTRACT FROM AUTHOR]

Nowadays, more and more optimization techniques are used to deal with complex engineering optimization problems. Firefly algorithm (FA) inspired by the flash communication between fireflies, has been proven to be competitive with other swarm intelligence algorithms and has been widely applied to solve complex engineering optimization problems. However, FA has some defects in dealing with complex engineering optimization problems, such as the exploration and exploitation cannot be well balanced. Therefore, in order to achieve effective performance, the different characteristics of search strategies can be applied at different stages of the search process to achieve a balance between exploration and exploitation. In this paper, a multi-strategy firefly algorithm with selective ensemble (MSEFA) is proposed. In MSEFA, the algorithm has three novel search strategies with different characteristics in the strategy pool. In addition, an idea of selective ensemble is adopted to design a priority roulette selection method. The method can select suitable search strategies in different search stages and coordinate the balance of strategies so that better results can be obtained. Furthermore, a parameter adaptive transformation mechanism is designed to control the decreasing rate of step size α. To verify the effectiveness of MSEFA, performance tests are conducted on the CEC 2013 and CEC 2019 test suites, after which MSEFA is used to solve four complex engineering optimization problems. Experimental results show that MSEFA has the best performance compared with other FA variants and other improved swarm intelligence algorithms. In addition, MSEFA also achieves the best results in dealing with four complex engineering optimization problems. • The proposed MSEFA algorithm can obtain the best results when dealing with optimization problems. • Three novel search strategies with different characteristics in this paper are presented. • A priority roulette selection method is designed to select strategies. [ABSTRACT FROM AUTHOR]

We introduce a multidimensional multiblock clustering (MDMBC) algorithm in this paper. MDMBC can generate overlapping clusters with similar values along clusters of dimensions. The parsimonious binary vector representation of multidimensional clusters lends itself to the application of efficient meta-heuristic optimization algorithms. In this paper, a hill-climbing (HC) greedy search algorithm has been presented that can be extended by several stochastic and population-based meta-heuristic frameworks. The benefits of the algorithm are demonstrated in a bi-clustering benchmark problem and in the analysis of the Leiden higher education ranking system, which measures the scientific performance of 903 institutions along four dimensions of 20 indicators representing publication output and collaboration in different scientific fields and time periods. • Multiblock clustering algorithm generates overlapping clusters along clusters of dimensions. • Generalized parsimonious binary vector-based representation for higher-dimensions. • The proposed algorithms are demonstrated via an open multidimensional dataset. [ABSTRACT FROM AUTHOR]

Multimodal multi-objective optimization (MMO) can offer more elegant solutions and provide diverse decisions to decision-makers in real world optimization problems. Many multimodal evolutionary mechanisms have been proposed to explore and exploit two solution spaces (i.e. decision space and objective space) in recent years. However, most existing methods only use single evolutionary operator to generate offsprings and ignore the advantage of using hybrid evolutionary algorithm. Moreover, it is still a great challenge to balance the effectiveness and efficiency simultaneously in the evolutionary process of MMO. In view of this, an efficient Two-Archive model based multimodal evolutionary algorithm is proposed in this paper. Two parallel offspring generation mechanisms based on competitive particle swarm optimizer and differential evolution are applied to expand two solution spaces with different evolutionary requirements. Moreover, niching local search scheme and reverse vector mutation strategy play roles in achieving better convergence and diversity. Finally, 22 MMO test problems are used to validate the superiority of the proposed method by comparing it with 5 state-of-the-art MMO algorithms. The proposed method is also expanded to solve 9 feature selection problems for validating the effectiveness of the proposed method on real world applications. • Two offspring generation mechanisms based on competitive PSO and DE are proposed. • Two diversity maintenance strategies named niching local search scheme and reverse vector mutation strategy are designed. • This paper proposes a novel multimodal multi-objective optimization algorithm with a new Two-Archive model. • Experiments validate that the proposed method can solve MMOPs effectively and efficiently with a relatively smaller population size. • The proposed algorithm is expanded to deal with 9 real world feature selection problems. [ABSTRACT FROM AUTHOR]

This paper considers an optimal control problem of switched dynamical systems with control input and system state constraints. Unlike in traditional switched dynamical systems, the switching times cannot be specified directly and they are governed by a state-dependent switching condition. Thus, the existing methods cannot be directly used to solve this problem. To overcome this difficulty, the switching conditions are transformed into a continuous-time inequality constraint by introducing an integer constraint. Further, the original optimal control problem is approximated by using a sequence of constrained non-convex nonlinear parameter optimization problems by using a relaxation method, a control vector parameterization technique, and a time-scaling transformation. Following that, a penalty function-based intelligent optimization algorithm is proposed for obtaining a global optimal solution based on a more effective penalty function method and a more effective intelligent optimization algorithm. The convergence results show that the proposed method is globally convergent. Numerical simulation results show that the proposed method is lower time-consuming, has faster convergence speed, can obtain a better objective function value than the existing typical algorithms, and can achieve a stable and robust performance when considering the small perturbations in constraint conditions or the small perturbations of the model parameters. • The switching times cannot be specified directly and they are governed by a state-dependent switching condition. • The switching conditions are transformed into a continuous-time inequality constraint by introducing an integer constraint. • A penalty function-based intelligent optimization algorithm is developed for solving this optimal control problem. • The convergence results show that this algorithm is globally convergent. • A drug therapy problem is provided to illustrate the effectiveness of the method developed by this paper. [ABSTRACT FROM AUTHOR]

An emerging challenge of most electric utilities is to expand hosting capacity constraints and maximise the interconnection of distributed energy resources (DER). DERs have proliferated in distribution systems in most jurisdictions, and this proliferation has not occurred in an orderly fashion. Reports of degradation in power quality and operations are common. With the continuing adoption of DERs, it is imperative to develop methods to unlock the potential of DERs in a way that they are used to support system needs, rather than degrading them. This concept's advantages are varied in different power system scenarios based on the considered models for owning and operating DER. With this objective in mind, this paper proposes a new algorithm to identify the optimal locations for new DERs. The proposed method is a novel combination of teaching–learning-based optimisation (TLBO) and honey-bee-mating optimisation (HBMO) algorithms, and it capitalises on both techniques' benefits. A major novelty of this method is that it fully accounts for current hosting capacity and all existing DERs and incorporates these parameters in the optimisation algorithm. The results reveal that adding fuzzy clustering to the multiobjective process improves the DER placement problem. The DERs considered in this optimisation problem are fuel cell units, and the objective function includes cost, losses, and voltage deviation. The proposed technique is adopted in the IEEE 70-bus-radial test system, and its performance is compared with those of other optimisation algorithms. The results reveal the proposed algorithm's superiority in accuracy and computational speed when reaching the optimal solution. As a case in point, the calculation time of the proposed algorithm is about %9 and %22 faster than TLBO and HBMO, respectively. • In this paper, a methodology for the integration of DERs is presented. • TLBO-HBMO improves the robustness, accuracy, and velocity of HBMO and TLBO. • TLBO-HBMO overcomes the unwanted local convergence of HBMO and TLBO algorithms. • Former installed DER, and hosting capacity of the distribution system are supposed. [ABSTRACT FROM AUTHOR]

With the availability of multi-material additive manufacturing, topology optimization of a multi-material structure and the selection of candidate materials become increasingly important. This paper aims to develop a new multi-material topology optimization algorithm and propose the guidelines for the selection of candidate materials from the database. The multi-material design variables and their inequality relationship are built on volume fractions of multiple materials within each element. The multi-material design variables are relaxed and multiple floating projection constraints simulate their discrete constraints by pushing design variables towards 0 or 1. Meanwhile, their inequality relationship is enforced by their variation limits. The proposed multi-material topology optimization algorithm can be applied to the compliance minimization problem constrained by a single mass or multiple volumes, as demonstrated in numerical examples. This paper mainly focuses on a single mass constraint, and 2D and 3D numerical examples systematically demonstrate that an optimized design under a mass constraint achieves a lower compliance when more materials appear in the final design simultaneously. Furthermore, we establish an approach to predict the inclusion or exclusion of a material from the final design, and propose the conditions for the co-existence of candidate materials, which guide users to select candidate materials from the database. • A new multi-material topology optimization algorithm is proposed. • The algorithm can be directly applied to a single mass constraint or multiple volume constraints. • The results systematically demonstrate the advantage of the multi-material design. • The approach to predict the occurrence of candidate materials in the final design is developed. • The approach can guide the users to select materials for multi-material additive manufacturing. [ABSTRACT FROM AUTHOR]

Metaheuristic optimization methods have been implemented for solving several problems. However, when it is required to implement those algorithms in hardware to run online, there is not enough information. This paper describes how could be programmed and implemented those optimization algorithms. Moreover, a complete evaluation is shown as well as a comparative study regarding the most important metaheuristic optimization algorithms. Thus, this paper presents a comparison between five optimization algorithms implemented into a cRIO field-programmable gate array (LabVIEW FPGA) NI-9030 of National Instruments T M (NI). The algorithms implemented were particle swarm optimization (PSO), bat algorithm (BA), grey wolf optimizer (GWO), earthquake algorithm (EA), and Nelder–Mead algorithm (NM). To analyze hardware device utilization and execution time by each algorithm, synthesis results were presented. In addition, a set of ten benchmark functions was selected to compare performance between algorithms. Results show the feasibility of this approach for NI FPGA hardware. From device utilization results, GWO presents the lowest placed usage (29%) while NM shows the fastest execution time (0.683 ms). Nevertheless, PSO, GWO and EA show better performance between benchmark functions due their exploration characteristics which make possible to find a better solution. [Display omitted] • Information about optimization implementation in hardware to run online is not enough • A hardware implementation of five optimization algorithms is described. • Five optimization algorithms were implemented into a cRIO LabVIEW FPGA. • Complete and comparative studies of those optimization algorithms are presented. • EA and GWO optimization algorithms are first time implemented into a FPGA. [ABSTRACT FROM AUTHOR]

The urban heat effect brings with a series of challenges for urban ecology and population health. In order to mitigate the adverse impacts of the urban heat effect quantitatively and effectively, a new land use search and exchange strategy is proposed to improve the multi-type ant colony algorithm in this paper and applied to a case study of Kunming, China. The results reveal the following: (1) the optimization algorithm can quickly locate the high-temperature area of the city and optimize the land use composition of this area; (2) the mean surface temperature of the core area can be reduced by 0.43–0.85 °C after land use optimization; and (3) Compared with Pareto optimization strategy and particle swarm optimization algorithm, the method proposed in this paper can achieve better optimization effect, higher computing efficiency and stability. This method can provide an effective scientific basis for sustainable urban planning and design, urban ecology and livability, and other urban retrofits. • The improved MACO algorithm can obtain high similiarity in fewer iterations. • The improved MACO algorithm can quickly locate the high-temperature area. • The improved MACO algorithm outperforms the Pareto strategy and PSO algorithm. [ABSTRACT FROM AUTHOR]