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Aiming at the problem that the sparrow search algorithm (SSA) does not have high optimization accuracy and is prone to fall into local optimum, an improved sparrow search algorithm with adaptive multi-strategy hierarchical mechanism is proposed (ISSA). Firstly, in the initialization phase, the population is created by combining the triangular topology and Logistic Chaos mapping, and an elite dynamic reverse learning strategy is used to enhance the population diversity and balance the local and global search performance. Secondly, an adaptive multi-strategy hierarchical mechanism is applied to the population, where adaptive dynamic adjustment strategy is applied to the discoverers to improve the flexibility and search efficiency of the algorithm; differential mutation operation is applied to the followers to generate a mutated subpopulation, which enhances the ability of the algorithm to jump out of the local optimum; and vertical and horizontal crossover strategies are applied to the vigilantes, where horizontal crossover enhances the global search ability, and vertical crossover maintains the population diversity and prevents the algorithm from falling into local optimality. Finally, the classical benchmark functions as well as the CEC2020 and CEC2022 test functions are selected for simulation and analysis, and the ISSA is compared with other optimization algorithm, and the ANOVA analysis, the Wilcoxon rank-sum test, and the Friedman test are performed. The simulation results show that the ISSA proposed in this paper achieves significant improvement in both convergence accuracy and convergence speed. Meanwhile, the application of ISSA to engineering problems fully verifies its practical value and significant advantages in the field of engineering problems. [ABSTRACT FROM AUTHOR]

Abstract To address the challenges of interference between network channels and low-frequency resource utilization efficiency, and to improve the overall performance and resource allocation fairness of the network, this study proposes a wireless communication network overlapping channel allocation strategy based on a discrete particle swarm optimization algorithm. First, the overlapping channel allocation problem in wireless networks is abstracted as a linear programming model with specific constraints, with the core objective of minimizing weighted interference between links. To solve this model, a discrete particle swarm optimization algorithm is introduced. In this algorithm, each particle’s position corresponds to a feasible overlapping channel allocation scheme, which is the potential solution to the optimization problem; The velocity of particles reflects the process of transitioning from the current channel configuration to the target channel configuration. Subsequently, based on the characteristics of discrete variables, corresponding operation rules and particle motion equations were defined. In addition, when particle swarm aggregation occurs, an optimal asymptotic mutation operator is introduced, which not only enhances the local search ability of the algorithm but also strengthens its global search ability. During algorithm execution, this mutation operator is applied based on a certain probability of mutation. The experimental results show that when using this strategy for overlapping channel allocation in wireless communication networks, even with an increase in the number of traffic flows, the average network throughput can remain stable, demonstrating a high network carrying capacity. Meanwhile, wireless networks exhibit a higher fairness factor, indicating a more balanced allocation of network resources among different users. In the process of optimal channel allocation, the packet loss rate reaches the lowest level, ensuring the reliability of data transmission.

The stagnation problem in evolutionary algorithms reduces the optimization algorithm's performance after a certain number of iterations, leading to diversity loss. In this article, five new mutation operators using Random, Random-Best, Best, Current Random, and Current-Best-Random selection policies based on dynamic motion strategy according to feasible environments from global search space are added to the Differential Evolution DE) process. The proposed strategy supports the diversity of the problem's nature while maintaining the global search and preserving environmental balance. The validation process has involved two tests. First, the testing and comparative study results showed that the proposed method performed satisfactorily at the target value of 10 - 8 for the bare minimum of statistical and function evaluations. In the second validation test, the proposed operators pass the Wilcoxon rank-sum test for the lowest error in each function (p = 0.05). The proposed approaches show an improvement in the search capability of evolutionary optimization algorithms. [ABSTRACT FROM AUTHOR]

Industrial applications and optimization problems in reality often involve multiple objectives. Due to the high dimensionality of objective space in many-objective optimization problems (MaOPs), the ability of traditional evolution operators to search the optimal region and generate promising offspring sharply decreases. Besides, as the number of objectives increases, it becomes difficult to balance the convergence and diversity of the population. Considering all these facts, this paper proposes a mutation individual position detection strategy. It estimates both individual fitness and diversity contributions, and assigns appropriate positions to individuals in the mutation operator through individual ranking. Then, by introducing an external population to adjust the weight vectors, its maintenance process takes into account the matching information between the population and the weight vectors. By comparing five representative algorithms, numerical experiments have shown that the algorithm can obtain a well distributed final solution set on optimization problems of various objective scales. Moreover, it also demonstrates advantages in generating excellent offspring individuals and balancing the overall performance of the population. In summary, the algorithm has competitiveness in solving MaOPs. [ABSTRACT FROM AUTHOR]

Differential evolution algorithm is a very powerful and recently proposed evolutionary algorithm. Generally, only a mutation operator and predefined parameter values of differential evolution algorithm are utilized to solve various optimization problems, which limits the performance of the algorithm. In this paper, six commonly used mutation operators are divided into three categories according to their own features. A mutation pool is established based on the three categories. A parameter pool with three predefined values is designed. During evolution, three mutation operators are randomly chosen from the three categories, and three parameter values are also randomly selected from the parameter pool. The three groups of mutation operators and parameter values are employed to produce trial vectors. The proposed algorithm makes good use of different mutation operators. Three recently proposed differential evolution variants and three non-differential evolution algorithms are used to make comparisons on the 29 testing functions from CEC. The experimental results have demonstrated that the proposed algorithm is very competitive. The proposed algorithm is utilized to solve three real applications, and the results are superior. [ABSTRACT FROM AUTHOR]

The moth search algorithm (MSA) is a meta-heuristic optimization technique inspired by moth behavior, has shown remarkable efficacy in solving optimization challenges. However, its poor exploration capability results in an imbalance between exploitation and exploration. To address this issue, this research introduces a new mutation operator to enhance exploration by increasing population diversity. The proposed enhanced moth search algorithm (EMSA) aims to expedite convergence and improve overall robustness by exploring new solutions more effectively. Evaluation on ten benchmark functions demonstrates EMSA's superior exploration capabilities, efficiently tackling optimization problems and yielding more optimal solutions within the search space. Compared to conventional MSA and other established algorithms, EMSA delivers well-balanced results, showcasing its effectiveness in optimizing the search space. In the future, the EMSA could potentially find applications in addressing real-world engineering optimization challenges. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a preventive maintenance decision model for electric vehicle charging stations based on mutation operators and lifecycle optimization to address the impact of potential faults on maintenance effectiveness. By introducing the particle swarm optimization algorithm with mutation operator, a comprehensive analysis of opportunity service age factor and safety failure probability factor was conducted to establish an indicator system for the operation status of charging piles, and a potential fault identification model was constructed. By optimizing the life cycle, the balance problem between optimal maintenance life and optimal opportunity maintenance life has been solved, thus completing preventive maintenance decisions. The experimental results show that the accuracy of this method in preventive maintenance decision‐making for electric vehicle charging piles can reach 98%, with an average preventive maintenance decision‐making time of 1.6 s for load piles. At the same time, the risk probability value and load loss value are effectively controlled. This study has good application prospects in improving the preventive maintenance effect of electric vehicle charging piles.

Hydraulic turbine control system is a complex system with strong nonlinearity and multiple variables. Therefore, in order to better control the turbine system, it is necessary to obtain the parameters of key components. Aiming at the limitation of the traditional particle swarm optimization algorithm in global search ability, mutation operator and dynamic inertia weight coefficient are introduced to enhance the search ability of the algorithm. In addition, in order to further improve the global search performance of the algorithm, this paper combines the optimized particle swarm optimization algorithm with genetic algorithm to form a hybrid parameter identification algorithm. The hybrid algorithm not only uses the fast convergence of particle swarm optimization (PSO), but also uses the global search advantage of genetic algorithm (GA) to realize efficient and accurate identification of turbine torque and load parameters. Through MATLAB2021a/Simulink simulation experiments, the application effect of the algorithm in the identification of turbine torque and generator load parameters is verified. The simulation results show that the optimized particle swarm optimization algorithm has significant advantages in the accuracy and robustness of parameter identification, and the identified parameters have a high degree of fitting with the actual measured torque and speed. This study not only provides a new optimization strategy for the parameter identification of turbine regulation system, but also provides an effective intelligent algorithm solution for the parameter identification of nonlinear system. [ABSTRACT FROM AUTHOR]

This paper proposes a preventive maintenance decision model for electric vehicle charging stations based on mutation operators and lifecycle optimization to address the impact of potential faults on maintenance effectiveness. By introducing the particle swarm optimization algorithm with mutation operator, a comprehensive analysis of opportunity service age factor and safety failure probability factor was conducted to establish an indicator system for the operation status of charging piles, and a potential fault identification model was constructed. By optimizing the life cycle, the balance problem between optimal maintenance life and optimal opportunity maintenance life has been solved, thus completing preventive maintenance decisions. The experimental results show that the accuracy of this method in preventive maintenance decision‐making for electric vehicle charging piles can reach 98%, with an average preventive maintenance decision‐making time of 1.6 s for load piles. At the same time, the risk probability value and load loss value are effectively controlled. This study has good application prospects in improving the preventive maintenance effect of electric vehicle charging piles. [ABSTRACT FROM AUTHOR]

A mutation operator in a real-coded genetic algorithm is developed and applied for efficient bridge-model optimization. A mutation operator that changes uniformly or dynamically with a crossover operator is proposed to address optimization problems. The performance of the combined genetic operators was verified using a variety of available test problems based on the convergence and search speed of the global optimal solution. It is shown that the genetic algorithm proposed in this study yields relatively better results than the available algorithms and is more effective in constrained optimization problems. The performance of the proposed genetic algorithm is also verified through a sample study using a field load test for the model optimization of an existing bridge. [ABSTRACT FROM AUTHOR]

Genetic algorithms (GAs) are very goodmetaheuristic algorithms that are suitable for solving NP-hard combinatorial optimization problems. A simpleGA begins with a set of solutions represented by a population of chromosomes and then uses the idea of survival of the fittest in the selection process to select some fitter chromosomes. It uses a crossover operator to create better offspring chromosomes and thus, converges the population. Also, it uses a mutation operator to explore the unexplored areas by the crossover operator, and thus, diversifies the GA search space. A combination of crossover and mutation operators makes the GA search strong enough to reach the optimal solution. However, appropriate selection and combination of crossover operator and mutation operator can lead to a very good GA for solving an optimization problem. In this present paper, we aim to study the benchmark traveling salesman problem (TSP). We developed several genetic algorithms using seven crossover operators and six mutation operators for the TSP and then compared them to some benchmark TSPLIB instances. The experimental studies show the effectiveness of the combination of a comprehensive sequential constructive crossover operator and insertion mutation operator for the problem. The GA using the comprehensive sequential constructive crossover with insertion mutation could find average solutions whose average percentage of excesses from the best-known solutions are between 0.22 and 14.94 for our experimented problem instances. [ABSTRACT FROM AUTHOR]

In this paper, an ensemble metaheuristic algorithm (denoted as LX-BBSCA) is introduced. It combines the strengths of Laplacian biogeography-based optimization (LX-BBO) and the sine cosine algorithm (SCA) to address structural engineering design optimization problems. Our primary objective is to mitigate the risk of getting stuck in local minima and accelerate the algorithm's convergence rate. We evaluate the proposed LX-BBSCA algorithm on a set of 23 benchmark functions, including both unimodal and multimodal problems of varying complexity and dimensions. Additionally, we apply LX-BBSCA to tackle five real-world structural engineering design problems, comparing the results with those obtained using other metaheuristics in terms of objective function values and convergence behavior. To ensure the statistical validity of our findings, we employ rigorous tests such as the t-test and the Wilcoxon rank test. The experimental outcomes consistently demonstrate that the ensemble LX-BBSCA algorithm outperforms not only the basic versions of BBO, SCA and LX-BBO but also other state-of-the-art metaheuristic algorithms.

The brain storm optimization (BSO) algorithm has received increased attention in the field of evolutionary computation. While BSO has been applied in numerous industrial scenarios due to its effectiveness and accessibility, there are few theoretical analysis results about its running time. Running-time analysis can be conducted through the estimation of the upper bounds of the expected first hitting time to evaluate the efficiency of BSO. This study estimates the upper bounds of the expected first hitting time on six single individual BSO variants (BSOs with one individual) based on the average gain model. The theoretical analysis indicates the following results. (1) The time complexity of the six BSO variants is O (n) in equal coefficient linear functions regardless of the presence or absence of the disrupting operator, where n is the number of the dimensions. Moreover, the coefficient of the upper bounds on the expected first hitting time shows that the single individual BSOs with the disrupting operator require fewer iterations to obtain the target solution than the single individual BSOs without the disrupting operator. (2) The upper bounds on the expected first hitting time of single individual BSOs with the standard normally distributed mutation operator are lower than those of BSOs with the uniformly distributed mutation operator. (3) The upper bounds on the expected first hitting time of single individual BSOs with the U − 1 2 , 1 2 mutation operator are approximately twice those of BSOs with the U (− 1 , 1) mutation operator. The corresponding numerical results are also consistent with the theoretical analysis results. [ABSTRACT FROM AUTHOR]

The selection method of individuals is a crucial step in the mutation operator of differential evolution (DE). Typically, methods that select individuals with better fitness values are used to increase the exploitation ability of the algorithm. However, some researches have shown that incorporating distribution information of target space to measure diversity can improve the exploration ability of the algorithm. With this concept in mind, this paper presents an innovative approach called the adaptive stochastically ranking-based tournament selection method (ASR-TS). ASR-TS initially uses tournament selection and subsequently stochastically ranks selected individuals based on fitness and diversity measurements, leading to the determination of the tournament's winner. Furthermore, the stochastic ranking parameter is adaptively set based on the success rate of the previous generation to strike a balance between the exploitation and exploration abilities of the algorithm. The proposed ASR-TS method was tested on CEC 2013 benchmark functions in several original and improved DEs. To further validate the effectiveness of this method, the ASR-TS method was also tested on CEC 2022 benchmark functions as well as real-world problems. The experimental results demonstrate that the proposed ASR-TS method outperformed various other methods by a significant margin, which proves its efficiency and effectiveness in balancing exploration and exploitation. [ABSTRACT FROM AUTHOR]

While the theoretical analysis of evolutionary algorithms (EAs) has made significant progress for pseudo-Boolean optimization problems in the last 25 years, only sporadic theoretical results exist on how EAs solve permutation-based problems. To overcome the lack of permutation-based benchmark problems, we propose a general way to transfer the classic pseudo-Boolean benchmarks into benchmarks defined on sets of permutations. We then conduct a rigorous runtime analysis of the permutation-based (1 + 1) EA proposed by Scharnow et al. (J Math Model Algorithm 3:349–366, 2004) on the analogues of the LeadingOnes and Jump benchmarks. The latter shows that, different from bit-strings, it is not only the Hamming distance that determines how difficult it is to mutate a permutation σ into another one τ , but also the precise cycle structure of σ τ - 1 . For this reason, we also regard the more symmetric scramble mutation operator. We observe that it not only leads to simpler proofs, but also reduces the runtime on jump functions with odd jump size by a factor of Θ (n) . Finally, we show that a heavy-tailed version of the scramble operator, as in the bit-string case, leads to a speed-up of order m Θ (m) on jump functions with jump size m. A short empirical analysis confirms these findings, but also reveals that small implementation details like the rate of void mutations can make an important difference. [ABSTRACT FROM AUTHOR]

The p-Hub allocation problem is a classic problem in location assignment, which aims to optimize the network by placing Hub devices and allocating each demand node to the corresponding Hub. A mutation Transit search (TS) algorithm with the introduction of the black hole swallowing strategy was proposed to solve the p-Hub allocation problem. Firstly, the mathematical model for the p-Hub allocation problem is established. Six mutation operators specifically designed for p-Hub allocation problem are introduced to enhance the algorithm's ability to escape local optima. Additionally, the black hole swallowing strategy was incorporated into TS algorithm so as to accelerate its convergence rate while ensuring sufficient search in the solution space. The improved TS algorithm was applied to optimize three p-Hub location allocation problems, and the simulation results are compared with those of the basic TS algorithm. Furthermore, the improved TS algorithm is compared with the Honey Badger Algorithm (HBA), Sparrow Search Algorithm (SSA), Harmony Search Algorithm (HS), and Particle Swarm Optimization (PSO) to solve three of p-Hub allocation problems. Finally, the impact of the number of Hubs on the cost of three models was studied, and the simulation results validate the effectiveness of the improved TS algorithm. [ABSTRACT FROM AUTHOR]

In this paper, an ensemble metaheuristic algorithm (denoted as LX-BBSCA) is introduced. It combines the strengths of Laplacian biogeography-based optimization (LX-BBO) and the sine cosine algorithm (SCA) to address structural engineering design optimization problems. Our primary objective is to mitigate the risk of getting stuck in local minima and accelerate the algorithm's convergence rate. We evaluate the proposed LX-BBSCA algorithm on a set of 23 benchmark functions, including both unimodal and multimodal problems of varying complexity and dimensions. Additionally, we apply LX-BBSCA to tackle five real-world structural engineering design problems, comparing the results with those obtained using other metaheuristics in terms of objective function values and convergence behavior. To ensure the statistical validity of our findings, we employ rigorous tests such as the t-test and the Wilcoxon rank test. The experimental outcomes consistently demonstrate that the ensemble LX-BBSCA algorithm outperforms not only the basic versions of BBO, SCA and LX-BBO but also other state-of-the-art metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Abstract Mutation-based greybox fuzzing has been one of the most prevalent techniques for security vulnerability discovery and a great deal of research work has been proposed to improve both its efficiency and effectiveness. Mutation-based greybox fuzzing generates input cases by mutating the input seed, i.e., applying a sequence of mutation operators to randomly selected mutation positions of the seed. However, existing fruitful research work focuses on scheduling mutation operators, leaving the schedule of mutation positions as an overlooked aspect of fuzzing efficiency. This paper proposes a novel greybox fuzzing method, PosFuzz, that statistically schedules mutation positions based on their historical performance. PosFuzz makes use of a concept of effective position distribution to represent the semantics of the input and to guide the mutations. PosFuzz first utilizes Good-Turing frequency estimation to calculate an effective position distribution for each mutation operator. It then leverages two sampling methods in different mutating stages to select the positions from the distribution. We have implemented PosFuzz on top of AFL, AFLFast and MOPT, called Pos-AFL, -AFLFast and -MOPT respectively, and evaluated them on the UNIFUZZ benchmark (20 widely used open source programs) and LAVA-M dataset. The result shows that, under the same testing time budget, the Pos-AFL, -AFLFast and -MOPT outperform their counterparts in code coverage and vulnerability discovery ability. Compared with AFL, AFLFast, and MOPT, PosFuzz gets 21% more edge coverage and finds 133% more paths on average. It also triggers 275% more unique bugs on average.

Resource selection, sharing, and aggregation are the key functions of grid computing. However, managing the resources in a grid-based environment is a stimulating task. It is necessary to update the topographical dispersal of the resources possessed by the various organisations with proper load distribution, and availability patterns. Different types of Users and servers have specific objectives and needs that could be achieved using a grid environment. This article suggests a cost-effective efficient framework for resource management in grid computing to look at and address the resource management difficulties. The proposed framework has three main functions, which help in grid construction, load balancing, and resource allocation. A Genetic engineering approach has been implemented to establish a relationship between the resource pool and the jobs of the nodes that improve resource utilization. The proposed methodology also optimizes the overall cost by minimizing turnaround time. The results of the proposed research are compared with commonly used algorithms and claim 1.5 to 10% better results.

Mutation-based greybox fuzzing has been one of the most prevalent techniques for security vulnerability discovery and a great deal of research work has been proposed to improve both its efficiency and effectiveness. Mutation-based greybox fuzzing generates input cases by mutating the input seed, i.e., applying a sequence of mutation operators to randomly selected mutation positions of the seed. However, existing fruitful research work focuses on scheduling mutation operators, leaving the schedule of mutation positions as an overlooked aspect of fuzzing efficiency. This paper proposes a novel greybox fuzzing method, PosFuzz, that statistically schedules mutation positions based on their historical performance. PosFuzz makes use of a concept of effective position distribution to represent the semantics of the input and to guide the mutations. PosFuzz first utilizes Good-Turing frequency estimation to calculate an effective position distribution for each mutation operator. It then leverages two sampling methods in different mutating stages to select the positions from the distribution. We have implemented PosFuzz on top of AFL, AFLFast and MOPT, called Pos-AFL, -AFLFast and -MOPT respectively, and evaluated them on the UNIFUZZ benchmark (20 widely used open source programs) and LAVA-M dataset. The result shows that, under the same testing time budget, the Pos-AFL, -AFLFast and -MOPT outperform their counterparts in code coverage and vulnerability discovery ability. Compared with AFL, AFLFast, and MOPT, PosFuzz gets 21% more edge coverage and finds 133% more paths on average. It also triggers 275% more unique bugs on average. [ABSTRACT FROM AUTHOR]

The Multi-Objective Bike Routing Problem (MOBRP) addressed in this paper consists in finding a set of good solutions that represents the trade-off between cyclist preferences when choosing a route. We propose a new genetic approach to solve the MOBRP (NGA-MOBRP) with a new mutation operator. To test the approach, we consider four conflicting criteria: safety, travel distance, travel time, and comfort. The well-known Nondominated Sorting Genetic Algorithm II (NSGA-II) is also implemented in the MOBRP context, and its weaknesses are identified and mitigated in the proposed NGA-MOBRP. The developed approach is compared with other published works in this field, namely another genetic algorithm and a Multi-Objective Simulated Annealing (MOSA) approach. The computational results show that the approach developed in this work obtains better results than the previously mentioned published approaches. [ABSTRACT FROM AUTHOR]

Swarm intelligence algorithms have superior performance in searching for the optimal feature subset, where Monarch Butterfly Optimization (MBO) can solve the continuous optimization problem. However, there exist some defects for MBO such as the limited searchable positions, falling into local optimum easily and unsolved binary variables. To address these drawbacks, this paper develops two mechanisms to propose several revisions of binary MBO (BMBO) for metaheuristic feature selection. First, to make MBO suitable to solve the feature selection optimization problems, the S-shaped and V-shaped transfer functions are introduced to convert continuous space into binary, and then force the butterfly to move in the binary search space. Two updated positions of the monarch butterfly population are designed based on these above transfer functions respectively to construct two BMBO models, namely BMBO-S and BMBO-V, as the first mechanism of BMBO. Second, the new step length parameter is proposed to update the position of monarch butterfly individuals. To prevent MBO from falling into the local optimum, the local disturbance and group division strategies are added into MBO to construct new BMBO method. It follows that a mutation rate is employed to enhance the detection stage of BMBO, and the mutation operator-based BMBO (BMBO-M) is designed to avoid the premature convergence of MBO. Third, this fitness function is integrated with the KNN classifier and the weight of the feature subset length to rank the selected feature subset, and a metaheuristic feature selection algorithm with BMBO-M is developed. Experiments applied to nineteen low dimensional UCI datasets and seven high dimensional datasets demonstrate our designed algorithm has great classification efficiency when compared with the other related technologies. [ABSTRACT FROM AUTHOR]

Summary: For mutation testing, the huge cost of running test suites on a large number of mutants has been a serious obstacle. To resolve this problem, we propose a learning‐based mutant reduction technique MuTrain. MuTrain uses cost‐considerate linear regression (i.e., CLARS) to learn a mutation model, which predicts the mutation score of a test suite based on the mutation testing results of a previous version of a target program. Then, MuTrain applies the mutation model for subsequent versions to predict mutation scores with significantly fewer mutants. For effective mutant reduction and accurate mutation score prediction, MuTrain uses fine‐grained mutation operators refined from the existing coarse‐grained mutation operators. The experiment results show that MuTrain reduces the number of mutants effectively (i.e., selecting only 1.6% of mutants). Moreover, MuTrain predicts mutation score far more accurately than the existing mutant reduction techniques and random mutant selection. We also found that MuTrain achieves much greater mutant reduction when it uses the fine‐grained mutation operators than the traditional coarse‐grained mutation operators (i.e., 1.6% vs. 14.6%). [ABSTRACT FROM AUTHOR]

To deal with the radio frequency threat posed by modern complex radar networks to aircraft, we researched the unmanned aerial vehicle (UAV) formations radar countermeasures, aiming at the solution of radar jamming resource allocation under system countermeasures. A jamming resource allocation method based on an improved firefly algorithm (FA) is proposed. Firstly, the comprehensive factors affecting the level of threat and interference efficiency of radiation source are quantified by a fuzzy comprehensive evaluation. Besides, the interference efficiency matrix and the objective function of the allocation model are determined to establish the interference resource allocation model. Finally, A mutation operator and an adaptive heuristic are integtated into the FA algorithm, which searches an interference resource allocation scheme. The simulation results show that the improved FA algorithm can compensate for the deficiencies of the FA algorithm. The improved FA algorithm provides a more scientific and reasonable decision-making plan for aircraft mission allocation and can effectively deal with the battlefield threats of the enemy radar network. Moreover, in terms of convergence accuracy and speed as well as algorithm stability, the improved FA algorithm is superior to the simulated annealing algorithm (SA), the niche genetic algorithm (NGA), the improved discrete cuckoo algorithm (IDCS), the mutant firefly algorithm (MFA), the cuckoo search and fireflies algorithm (CSFA), and the best neighbor firefly algorithm (BNFA).

Fuzzing has become the focus of research in the field of software testing due to its advantages such as simple testing process, easy deployment, and easy reproduction of crashes. However, fuzzing also suffers from the disadvantages of poor test purpose and invalid generated seeds. To solve the above problems, researchers proposed the Memory Usage Guided Fuzzing (MUGF). To further optimize the performance of MUGF, this paper proposes a fuzzing method named Memory Usage Guided Fuzzing based on Particle Swarm Optimization (MUGF-PSO). MUGF-PSO will guide the selection of mutation operators for subsequent testing by learning the effectiveness of each mutation operators during previous testing. Specifically, MUGF-PSO regards each mutation operator as a particle in the particle swarm optimization algorithm, and the coverage change is regarded as an important factor to evaluate the seed's local and global best position. In addition, the efficient selection probability distribution of mutation operator is constantly iteratively searched to make the MUGF tends to select the mutation operator that is more able to trigger new coverage. Furthermore, the MUGF-PSO is integrated into MemLock developed by MUGF, that is, MemLock-PSO is developed. We conduct a comparison experiment with 5 fuzzers (including AFL, MemLock, EcoFuzz, HavocMAB and Darwin), and the results show that MemLock-PSO is able to find more paths and crashes in 12 widely used program with different functions from 10 tools, with a significant gap of around 12 h. Meanwhile, the MemLock-PSO have a significant difference in the number of paths and crashes compared to other fuzzers with a good stability. Our work also proves the remarkable significance of applying swarm intelligence optimization algorithms in fuzzing in order to solve the problem of selection of mutation operators. [ABSTRACT FROM AUTHOR]

Functional Distributed Constraint Optimization Problems (F-DCOPs) are a constraint processing framework for continuous variables in the multi-agent system. Particle swarm optimization-based F-DCOP (PFD) is a population-based algorithm to solve F-DCOP collaboratively. Although it can significantly reduce the computational overhead and memory requirements, its solution depends on the decision of root agent in the Breadth First Search (BFS) pseudo-tree and it is easy to fall into local optimum. To solve the above problems, this paper designed an improved PFD algorithm with Local Decision named PFD-LD, which effectively reduces the dependence on root agent through local decision. In addition, a mutation operator is used to avoid falling into local optimum. It is proved that PFD-LD is an anytime algorithm and local decision can expand the search of the solution space. Finally, the extensive experiments based on four types of benchmark problems show that the proposed algorithm outperforms state-of-the-art F-DCOP solving algorithms. [ABSTRACT FROM AUTHOR]

The surface inspection of strip steel defects plays a vital role in the industry, and it has attracted widespread attention in the industry. In this paper, an improved sparrow search algorithm (WMR-SSA) with intelligent weighting factors and mutation operators is proposed, WMR-SSA can balance the development capability of the algorithm based on the number of iterations. In addition, WMR-SSA enhances the local search capability of the algorithm through mutation operators. At the same time, the algorithm determines the initial position of the population by random walk to enhance the diversity of the population. The WMR-SSA algorithm is compared with GA, PSO, CS, GWO, BSA, and original SSA, and the experiment proves that the WMR-SSA algorithm is better than other algorithms. In this study, WMR-SSA is combined with BP neural network and implemented for the classification of defective strip images. The accuracy and stability of WMR-SSA-BP are effectively demonstrated experimentally by comparing it with classifiers optimized by other intelligent algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a novel two layer differential evolutionary algorithm with multi-mutation strategy (TLDE) for solving the economic emission dispatch (EED) problem involving random wind power. In recent years, renewable energy such as wind power is more and more participated in the power systems to address the problems of fossil energy shortage and environmental pollution. Hence, the EED problem with the availability of random wind power is investigated in this paper. Due to the uncertain nature of wind speed, the Weibull probability distribution function is used to model the random wind power. In order to improve the search ability, TLDE divides the population into two layers according to the fitness ranking, and individuals in the two layers are treated differently to fully investigate their own potential. The two layers can cooperate with each other to further enhance the search performance by utilizing an information sharing strategy. Also, an adaptive restart scheme is introduced to avoid falling into stagnation. The performance of the proposed TLDE is testified on the 40 units system with 2 modified wind turbines. The experimental results demonstrate that the TLDE method can achieve precise dispatch strategy in EED problem with random wind power. [ABSTRACT FROM AUTHOR]

Seagull optimization algorithm (SOA) is a recent bio-inspired technique utilized to improve the constrained large-scale problems in low computational cost and quick convergence speed. However, the globally optimized search space for the SOA is linear, which means that the SOA's global search capability could not be fully utilized. Thus, we propose an improved SOA algorithm (ISOA) using Lévy flight and mutation operators. The ISOA obtains some Lévy flight features, which improves the original SOA by performing large jumps, making the search escape from the local optima and begin at a different search space region. The mutation operator, which improves the exploration–exploitation trade-off, allows the catch of the optimal solution quickly and accurately. In order to examine the performance of the proposed ISOA approach, three experiments were conducted. The first one evaluates the ISOA in solving the global optimization problem. The second one is a comparative study based on twenty benchmark datasets to evaluate the general capability of ISOA in feature selection, compared to ten recent and well-established algorithms constructed using the other meta-heuristics methods. Furthermore, the third experiment is conducted using a real dataset with various face poses to investigate the efficiency of the ISOA in pose-variation recognition. Compared to the other meta-heuristics methods, the results show that the proposed model is more accurate and efficient in global optimization, feature selection purposes, and pose variation recognition. Furthermore, the ISOA approach outperforms the other methods proposed in the state-of-the-art literature. [ABSTRACT FROM AUTHOR]

The mutation is one of the most important stages in the genetic algorithm (GA) because of its influence on exploring solution space and overcoming premature convergence. Since there are many types of mutation operators, the problem lies in selecting the appropriate type, and so, researchers usually need more trial and error. This paper investigates a new mutation operator based on teaching–learning-based optimization (TLBO) to enhance the performance of genetic algorithms. This new mutation operator treats intelligently instead of the random type, enhances the quality of solution, and speeds up the convergence of GA simultaneously. Several experiments are conducted on six standard test functions to evaluate the effect of the proposed mutation operator. First, proper comparisons are made between the performance of the proposed mutation to the classic mutation of GA and their combinatorial format. The result indicates the effect of the proposed mutation operator on the significant enhancement of the genetic algorithms' performance particularly. Due to computational analysis with Intel(R) Core(TM) i5-2430 M CPU @ 2.40 GHz processor, this method causes 32–53.3% reduction in essential iteration to present zero amount as the final value for four test functions (i.e., Beale, Himmelblau, Booth, and Rastrigin). For the two other functions that provides a non-zero value (i.e., Ackley and Sphere), the proposed method improves nearly 100% in average of objective. According to the result, the final solutions of the proposed method are equal or better than the classic GA in all six problems. Then, the performance of the proposed algorithm in comparison to five well-known algorithms ensures its superiority. In all comparisons, the proposed method performs equal or better than five other algorithms in CPU time and quality solutions. [ABSTRACT FROM AUTHOR]

Prediction models are used to prevent and prepare for corresponding events according to various types of data generated in production. Aiming at the problems of lower predictive accuracy and slower convergent speed of the existing prediction models, a prediction model based on fireworks algorithm (FWA) and long short-term memory (LSTM) is proposed to predict time-related data. Firstly, we establish the interconnection structure model of the hidden layer nodes in the LSTM. Then, considering the diversity and concurrency of the group, we optimize the hyperparameters combination of LSTM. Finally, we improve FWA by adding three mutation operators (Gaussian mutation operator, Cauchy mutation operator, and discrete mutation operator). Based on the enhanced FWA, we achieve a better optimization effect by increasing the diversity of hyperparameters combination. The experimental results show that the performance of the proposed LSTM-enhanced FWA model has been significantly improved by comparing to the existing LSTM and LSTM-GS models. The mean absolute error (MAE) is reduced by 38.49% and 17.79%, respectively; the root mean squared error (RMSE) is reduced by 29.47% and 19.28%, respectively; and the mean absolute percentage error (MAPE) is reduced by 36.67% and 26.92%, respectively. MAE and MAPE represent the degree of deviation between the predicted value and actual value, and RMSE well reflects the precision. This means that the proposed LSTM-enhanced FWA model is better than existing LSTM and LSTM-GS models because all three types of error are reduced, meanwhile three kinds of error better reflect accuracy and precision of error. [ABSTRACT FROM AUTHOR]

The widely employed mutation operator D E / c u r r e n t - t o - p b e s t / 1 in the differential evolution algorithm (DE) is further developed to a new version D E / c u r r e n t - t o - p b e s t / 1 - X in this paper. To test its performance, it has been embedded in the novel successful history-based adaptive DE (L-SHADE) and compared with other recently proposed mutation operators. In D E / c u r r e n t - t o - p b e s t / 1 - X , the updated parameter memories in each generation are not adopted when the initial value can still maintain an acceptable successful rate of finding better offspring. Also, the generated worse offsprings with acceptable fitness values are partially archived to generate differential vectors. The experimental results show that D E / c u r r e n t - t o - p b e s t / 1 - X has a comparable performance than D E / c u r r e n t - t o - p b e s t / 1 , D E / c u r r e n t - t o - o r d _ p b e s t / 1 and D E / c u r r e n t - t o - o r d _ b e s t / 1 . [ABSTRACT FROM AUTHOR]

This paper introduces an improved differential evolution algorithm based on cooperative multi-population (CMp-DE for short), which combines diverse population collaboration mechanisms and catalytic factors into an improved differential evolution framework. By harnessing various population collaboration mechanisms, the algorithm enhances the diversity of individuals within populations during initial iterations and reduces it during later iterations, thereby harmonizing the algorithm's exploratory and exploitative capabilities. Furthermore, a novel mutation operator is proposed that divides the iterative process into exploration and exploitation phases, thereby augmenting the algorithm's global exploration prowess. Lastly, a catalytic operator is introduced to generate new individuals near post-crossover individuals based on a specified rule, which enhances the algorithm's ability to escape local optima and increasing stability. The proposed CMp-DE is benchmarked against the CEC2017 benchmark test functions and compared against 13 algorithms, including five differential evolution algorithms and their variants, as well as eight state-of-the-art metaheuristic optimization algorithms. This evaluation assesses the CMp-DE's solution accuracy, convergence, stability, and scalability. Finally, the applicability of CMp-DE is validated by addressing six practical optimization problems. The experimental results show that CMp-DE surpasses other algorithms in terms of both convergence accuracy and robustness. Moreover, integrating a catalytic operator with other optimization algorithms notably boosts performance in convergence accuracy and stability. The inclusion of the catalytic operator has significantly enhanced the performance of algorithms compared to their performance before its addition. This underscores the potential of the catalytic operator in improving the performance of various algorithms, particularly in terms of convergence accuracy and robustness. [ABSTRACT FROM AUTHOR]

The fixed-charge transportation (FCT) problem, an extension of the classical transportation problem, holds significance in practical logistics scenarios where fixed charges play a crucial role. Fixed charges categorize it as an NP-hard problem, posing challenges for conventional methods due to their inefficiency, high computational costs, and susceptibility to local optima. This paper introduces an enhanced evolutionary algorithm, offline learning-based competitive swarm optimization (OLCSO), tailored to address the FCT problems efficiently. OLCSO draws inspiration from offline learning, where particles benefit from the experiences of high performers. The algorithm incorporates (i) a ranking-based mechanism where losers learn from peer-to-peer winners based on their ranks, (ii) a two-phase cooperative evolutionary mechanism enhancing searchability and convergence of the algorithm. In the first phase, losers learn from personal best experiences and the shifted center of the population, boosting diversity. In the second phase, losers learn from winners and the center of the population, improving exploitation, (iii) a mutation strategy is embedded to update winner locations in each evolutionary procedure, and (iv) to ensure a feasible solution to FCT problems, the paper also introduces negative and fractional repair mechanisms. OLCSO is applied to solve two sets of FCT problems, linear and non-linear. Experimental results are compared with various heuristic and metaheuristic techniques, commonly used for these problems, using diverse metrics. A comparative analysis of OLCSO's design elements is also conducted. The results demonstrate OLCSO's superiority over existing algorithms. [ABSTRACT FROM AUTHOR]

In recent years, there have been notable advancements in the theoretical research of existing meta-heuristic algorithms and in the development of novel algorithms. However, the research on the definition and construction methods of the so-called "mutation" operator, a concept widely used in existing meta-heuristic algorithms, remains notably insufficient. In addition, how to directly construct a meta-heuristic algorithm under the guidance of stability and invariance analysis continues to pose a challenge that has yet to resolve. This paper carefully investigates and rigorously defines the concepts of "mutation operator" and "mutation-driven meta-heuristic algorithm" as found in the literature. According to these definitions, a novel meta-heuristic algorithm, called Multi-direction guided Mutation-driven Stable swarm Intelligence algorithm with Translation and Rotation invariance (M2SITRI) is proposed, and the corresponding mutation operator is carefully designed based on the analysis of translation invariance, rotation invariance, order-1 stability, and order-2 stability. The stability and invariance properties of the M2SITRI algorithm are validated through numerical simulations, and its superior solving performance is demonstrated by benchmarking it against several other well-known existing meta-heuristic algorithms across a variety of test problems. • Mutation operator and mutation-driven meta-heuristic algorithm concepts are defined. • A novel translation and rotationally invariant meta-heuristic algorithm termed M2SITRI is proposed. • The invariance and stability properties and the excellent solving performance of M2SITRI algorithm are verified. [ABSTRACT FROM AUTHOR]

The major purpose of this article is to enhance the performance of GOA algorithm by integrating a new mutation operator to the standard GOA algorithm. A series of six different variants of enhanced GOA is proposed by integrating GOA with six different variants of the mutation operator. The new enhanced metaheuristic optimization method is called EGOAs. EGOA aims to address the problems of slow convergence and trapping into local optima, by achieving a good balance between exploration and exploitation, using a special mutation operator that enhances the diversity of the standard GOA, to find the best solution for global optimization problems. The implementation process for enhancing the GOA algorithm is presented and the effectiveness of the enhanced algorithm is evaluated against 60 of the optimization benchmark functions, and compared to that of the standard GOA, as well as to other metaheuristic optimization algorithms. The performance of EGOAs was compared with the other improved methods based on GOA. Experimental results show that EGOAs is clearly superior to the standard GOA algorithm, as well as to other well-known algorithms, in terms of achieving the best optimal value, convergence speed, and avoiding local minima, which makes EGOAs a promising addition to the arsenal of metaheuristic algorithms. [ABSTRACT FROM AUTHOR]

Feature selection (FS) is used to solve hard optimization problems in artificial intelligence and data mining. In the FS process, some, rather than all of the features of a dataset are selected in order to both maximize classification accuracy and minimize the time required for computation. In this paper a FS wrapper method that uses K-nearest Neighbor (KSN) classification is subjected to two modifications using a current improvement algorithm, the Monarch Butterfly Optimization (MBO) algorithm. The first modification, named MBOICO, involves the utilization of an enhanced crossover operator to improve FS. The second, named MBOLF, integrates the Lévy flight distribution into the MBO to improve convergence speed. Experiments are carried out on 25 benchmark data sets using the original MBO, MBOICO and MBOLF. The results show that MBOICO is superior, so its performance is also compared against that of four metaheuristic algorithms (PSO, ALO, WOASAT, and GA). The results indicate that it has a high classification accuracy rate of 93% on average for all datasets and significantly reduces the selection size. Hence, the findings demonstrate that the MBOICO outperforms the other algorithms in terms of classification accuracy and number of features chosen (selection size). [ABSTRACT FROM AUTHOR]

The Swarm based algorithms deliver better performance when it can maintain perfect balance of the exploration and exploitation process and converges faster by successfully avoiding local optima entrapment. At recent time, Crow Search Algorithm (CSA) is developed as a nature inspired swarm based algorithm. It can solve continuous, non-linear and complex day to day life optimization problems. Like many other optimization algorithms, CSA suffers with the problem of local stagnation. This paper introduces an improved version of the CSA, which improves the performance of the existing CSA algorithm by using oppositional learning and mutation operator. The proposed algorithm is termed as OBL-CSA-MO. The OBL-CSA-MO enhances the exploration and exploitation capability in the search space and successfully avoids local optima entrapment. The OBL-CSA-MO is evaluated by considering the IEEE CEC 2017 standard benchmark functions set. The efficiency and robustness of the proposed OBL-CSA-MO is measured using performance metrics, convergence analysis and statistical significance. A demonstration is given as an application of the proposed algorithm for designing fractional order PID controller as the real life problem. For this purpose, the first order and higher order plants are considered in the FOPID designing using the proposed OBL-CSA-MO. The experimental results demonstrate that the developed OBL-CSA-MO can be used for solving optimization problems effectively. [ABSTRACT FROM AUTHOR]

Nowadays, most of the communications in IoT enabled devices are done in the form of images. To protect the images from intruders, there is a need for a secure encryption algorithm. Many encryption algorithms have been proposed, some of the algorithms are based on symmetric-key cryptography and others are based on asymmetric key cryptography. This work proposed a fast, secure, and lightweight symmetric image cryptographic algorithm based on the session key. In this work, for every image encryption, a new session key is generated. Here session keys are generated with the help of crossover and mutation operators of genetic algorithm. This proposed algorithm uses a 64-bit plain text and requires an 80-bit key, where 64-bits of a key is generated via symmetric hexadecimal key and the remaining 16-bits of a key are randomly added, to encrypt the image. Here crossover and mutation operators are used to generate random 64-bits of a key. The proposed algorithm will work for both color and grayscale images. The proposed algorithm is simulated on MATLAB 2017 platform and compared with similar types of the existing algorithm on various parameters. [ABSTRACT FROM AUTHOR]

Logic minimization has recently attracted significant attention because in many applications it is important to have a compact representation as possible. In this paper, we propose a fast minimization algorithm (FMA) of fixed polarity Reed-Muller expressions (FPRMs). The main idea behind the FMA is to search the minimum FPRM with the fewest products by using the proposed binary differential evolution algorithm (BDE). The BDE can efficiently maintain population diversity and achieve a better tradeoff between the exploration and exploitation capabilities by use of proposed binary random mutation operator and improved selection operator. The experimental results on 24 MCNC benchmark circuits demonstrate that the FMA outperforms the genetic algorithm-based and simulated annealing genetic algorithm-based FPRMs minimization algorithms in terms of accuracy of solutions and solving efficiency. To the best of our knowledge, we are the first to use differential evolution algorithm to minimize FPRMs. The FMA can be extended to derive a minimum mixed polarity Reed-Muller expression.

Aiming at the problem of ease of falling into local optimum and low solution quality when solving optimization problems, this paper proposes an adaptive hybrid cuckoo search (AHCS) algorithm. AHCS improves the Lévy flight method and population evolution strategy of the cuckoo search (CS) algorithm, and introduces a mutation operation operator. Inspired by the idea of position update of particle swarm optimization (PSO) algorithm, this paper introduces the inertia weight w in the Lévy flight method of CS algorithm, and gives the new dynamic adjustment methods of parameters α and β respectively. In order to enhance the local search ability and optimization speed of the algorithm, this paper introduces the mutation operation operator, and presents a new evolution strategy of the hybrid cuckoo search algorithm. In addition, in order to verify the performance of AHCS, 30 benchmark functions and CEC 2017 optimization problems were selected. The calculation results of the 30 benchmark functions and CEC 2017 optimization problems show that compared with other algorithms, the number of winning cases of t-test values and the Friedman average ranking for AHCS are significantly better than other algorithms. Finally, AHCS and various intelligent optimization methods in the literature are used to optimize the structural parameters of the reducer and the cantilever beam. The optimization results show that the quality of AHCS solution is significantly better than other algorithms.

This paper proposes to resolve the limitation of differential evolution (DE) that the difference between the individuals in search behavior has not yet been utilized effectively for guiding the evolution of the population. An adaptive guiding mechanism (AGM) based on the heuristic rules is thus suggested to make possible, individual-dependent guidance. The AGM mainly comprises three stages: construction, separation, and guidance. In the construction stage, the elite leadership team (ELT) is established with an adaptive control scheme by using good information of the population. In the separation stage, the ELT is divided into distinct elite groups that are allocated to different individuals based on their search behaviors. In the guidance stage, the leader that is chosen from the respective elite group, as well as the promising directions extracted from the population, are used together to guide the search of each individual. By incorporating AGM into DE, a novel algorithm framework, named DE with AGM (DE-AGM), is proposed to enhance the performance of DE. As a general framework, DE-AGM can be easily and seamlessly applied to most DE variants. The experimental results on 58 benchmark functions have demonstrated the competitive performance of DE-AGM.

The problem of structures learning in Bayesian networks is to discover a directed acyclic graph that in some sense is the best representation of the given database. Score-based learning algorithm is one of the important structure learning methods used to construct the Bayesian networks. These algorithms are implemented by using some heuristic search strategies to maximize the score of each candidate Bayesian network. In this paper, a bi-velocity discrete particle swarm optimization with mutation operator algorithm is proposed to learn Bayesian networks. The mutation strategy in proposed algorithm can efficiently prevent premature convergence and enhance the exploration capability of the population. We test the proposed algorithm on databases sampled from three well-known benchmark networks, and compare with other algorithms. The experimental results demonstrate the superiority of the proposed algorithm in learning Bayesian networks.

Abstract Reasonable scheduling of flexible job shop is key to improve production efficiency and economic benefits; in order to solve the problem in flexible job shop scheduling problem, a novel flexible job shop scheduling method based on improved artificial immune algorithm is proposed. Firstly, a mathematical model of the flexible job shop scheduling is established, and the total shortest processing time is taken as the objective function. Secondly, artificial immune algorithm is used to solve the problem, and particle swarm optimization algorithm is taken as the operator to embed into manual immune algorithm for maintaining the diversity of population and prevent obtaining local optimal solution. Finally, the performance of the algorithm is tested by simulation experiments on standard set. The results show that the proposed algorithm can obtain better flexible job shop scheduling scheme and especially has more significant advantages in solving large-scale problems in comparison with other algorithms.

Accurate intrusion detection is necessary to preserve network security. However, developing efficient intrusion detection system is a complex problem due to the nonlinear nature of the intrusion attempts, the unpredictable behaviour of network traffic, and the large number features in the problem space. Hence, selecting the most effective and discriminating feature is highly important. Additionally, eliminating irrelevant features can improve the detection accuracy as well as reduce the learning time of machine learning algorithms. However, feature reduction is an NP-hard problem. Therefore, several metaheuristics have been employed to determine the most effective feature subset within reasonable time. In this paper, two intrusion detection models are built based on a modified version of the firefly algorithm to achieve the feature selection task. The first and, the second models have been used for binary and multiclass classification, respectively. The modified firefly algorithm employed a mutation operation to avoid trapping into local optima through enhancing the exploration capabilities of the original firefly. The significance of the selected features is evaluated using a Naïve Bayes classifier over a benchmark standard dataset, which contains different types of attacks. The obtained results revealed the superiority of the modified firefly algorithm against the original firefly algorithm in terms of the classification accuracy and the number of selected features under different scenarios. Additionally, the results assured the superiority of the proposed intrusion detection system against other recently proposed systems in both binary classification and multi-classification scenarios. The proposed system has 96.51% and 96.942% detection accuracy in binary classification and multi-classification, respectively. Moreover, the proposed system reduced the number of attributes from 41 to 9 for binary classification and to 10 for multi-classification. [ABSTRACT FROM AUTHOR]

Moth-Flame Optimization (MFO) algorithm is a new population-based meta-heuristic algorithm for solving global optimization problems. Flames generation and spiral search are two key components that affect the performance of MFO. To improve the diversity of flames and the searching ability of moths, an improved Moth-Flame Optimization (IMFO) algorithm is proposed. The main features of the IMFO are: the flames are generated by orthogonal opposition-based learning (OOBL); the modified position updating mechanism of moths with linear search and mutation operator. To evaluate the performance of IMFO, the IMFO algorithm is compared with other 20 algorithms on 23 benchmark functions and IEEE (Institute of Electrical and Electronics Engineers) CEC (Congress on Evolutionary Computation) 2014 benchmark test set. The comparative results show that the IMFO is effective and has good performance in terms of jumping out of local optimum, balancing exploitation ability and exploration ability. Moreover, the IMFO is also used to solve three engineering optimization problems, and it is compared with other well-known algorithms. The comparison results show that the IMFO algorithm can improve the global search ability of MFO and effectively solve the practical engineering optimization problems. [ABSTRACT FROM AUTHOR]

Constrained multi-objective optimization problems (CMOPs) are common in engineering design fields. To solve such problems effectively, this paper proposes a new differential evolution variant named IMDE with infeasible-guiding mutation operators and a multistrategy technique. In IMDE, an infeasible solution with lower objective values is maintained for each individual in the main population, and this infeasible solution is then incorporated into some common differential evolution's mutation operators to guide the search toward the region with promising objective values. Moreover, multiple mutation strategies and control parameters are adopted during the trial vector generation procedure to enhance both the convergence and the diversity of differential evolution. The superior performance of IMDE is validated via comparisons with some state-of-the-art constrained multi-objective evolutionary algorithms over 3 sets of artificial benchmarks and 4 widely used engineering design problems. The experiments show that IMDE outperforms other algorithms or obtains similar results. It is an effective approach for solving CMOPs, basically due to the use of infeasible-guiding mutation operators and multiple strategies. [ABSTRACT FROM AUTHOR]