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The multi-population method is a common method for solving dynamic optimization problems. However, to design an efficient multi-population method, one of the challenging issues is how to allocate computational resources between populations given a limited computing buget in dynamic environments. This paper designs a contribution-based resource allocation mechanism. In this mechanism, a contribution degree of a population is defined according to the performance of the population, which determines the probability of the population to obtain the computing resource. This mechanism is implemented in an adaptive multi-population method. Experimental results on the moving peaks benchmark show that the algorithm equipped with the resource allocation mechanism outperforms the original algorithms.

Real-time strategy (RTS) games have become one of the hotspots in the field of artificial intelligence research due to the large search space, long-term planning, and real-time constraint. In view of the fact that most of the current research on solving the RTS combat problem uses offline training methods, and these methods may need a lot of training time, this work uses an evolutionary algorithm (EA) to directly optimize the units’ action sequences online. In order to solve the problems of repetitive actions caused by the neutral area and the size correlation caused by cooperation between units, three search operators are proposed. It is shown that this method can effectively solve the combat problem in RTS games.

In 2016, "Students Development Key Competency of China" was officially published. China places great emphasis on establishing students thinking mode in digital environment, who can use information technology to solve problems and have the sense of social responsibility. In 2017, the Ministry of Education published a revised version of "High School Information Technology Curriculum Standard", which clarifies the information key competency, reconstructs the content and module of the high school information technology curriculum. In order to enable high school students to develop information key competency, information technology teachers’ key competency should be improved at first. This promotion needs to be accompanied by a systematic reform of pre-service cultivation. It is urgent to strengthen the training of information key competency of students majoring in computer education, who will be the information technology teachers in the future. This paper focuses on the information key competency of students majoring in computer education and analyse the present problems in the existing talent cultivation. Furthermore, we put forward the systematic reform measures for computer education major.

Balancing objectives optimization and constraints satisfaction are two equally important goals for constrained multi-objective optimization problems (CMOPs). However, most studies pay more attention on avoiding violating the constraints than achieving better objectives optimization. To alleviate this issue, this paper presents a dynamic constrained multi-objective evolutionary algorithm (DCMOEA) for handling constraints and optimizing objectives simultaneously. DCMOEA converts a m-objective COP to a dynamic (m+2)-objective COP. A simple yet effective dynamic niching technique is designed to enhance the population diversity from the decision space. An instantiation of the DCMOEA on NSGA-II (named DCNSGA-II) is implemented, and compared with five representative constraint-handling techniques on 26 well-known CMOPs. The experimental results indicate that the DCNSGA-II is highly competitive in solving CMOPs.

There are many approaches to non-dominated sorting, but they are computationally expensive when the number of objectives or the number of fronts becomes relatively large. This paper proposes an efficient non-dominated sorting approach based on a dominance ranking graph created in this paper. The approach takes advantages of both the dominated and non-dominated relationships, it is able to efficiently handle the case of large number of objectives/fronts. Experimental results show that the proposed algorithm outperforms its several popular variants in most of the tests.

For the differential evolution (DE) algorithms, there are many parameter adaptation methods, which aim at tuning the mutation factor F and the crossover probability CR. When the population diversity is very small and has been converged in a local optimum, even if the evolution goes on, the population will no longer improve. This is also true for the DE algorithms with adaptive F and CR. The enhancement of population diversity is necessary to DE algorithms. In this paper, we test the JADE algorithm with auto-enhanced population diversity (AEPD) on the newest benchmark functions.

The travelling salesman problem is a well known combinatorial optimization problem and evolutionary computation methods are one of the important methods to solve it. A difficult issue for evolutionary computation methods is to identify good edges that belong to the global optimum during the search progress. To address this issue, this paper proposes a tour construction algorithm, which is based on a memory-based statistical learning mechanism. A probability matrix is created according to the edge distribution in a memory population, which stores the best solution found by every individual. For each individual, a tour is constructed according to its local personal best solution found so far and the global probability matrix. Two variants of ant colony optimization are chosen to test the effectiveness of the proposed algorithm. The result show that the proposed algorithm is an efficient learning algorithm for the travelling salesman problem.

This paper presents a Modified Cuckoo Search (MCS) algorithm for solving flow shop scheduling problem with blocking to minimize the makespan. To handle the discrete variables of the job scheduling problem, the smallest position value (SPV) rule is used to convert continuous solutions into discrete job permutations. The Nawaz-Enscore-Ham (NEH) heuristic method is utilized for generating high quality initial solutions. Moreover, two frequently used swap and insert operators are employed for enhancing the local search. To verify the performance of the proposed MCS algorithm, experiments are conducted on Taillard's benchmark set. Results show that MCS performs better than the standard CS and some previous algorithms proposed in the literature.

In Differential Evolution (DE), there are many adaptive DE algorithms proposed for parameter adaptation. However, they mainly focus on tuning the mutation factor F and the crossover probability CR. The adaptation of population size NP has not been widely studied in the literature of DE. Reducing population size without jeopardizing the performance of an algorithm could save computational resources and hence accelerate it's convergence speed. This is beneficial to algorithms for optimization problems which need expensive evaluations. In this paper, we propose an improved population reduction method for DE, called dynNPMinD-DE, by considering the difference between individuals. When the reduction criterion is satisfied, dynNPMinD-DE selects the best individual and pairs of individuals with minimal-step difference vectors to form a new population. dynNPMinD-DE is tested on a set of 13 scalable benchmark functions in the number of dimensions of D=30 and D=50, respectively. The results show that dynNPMinD-DE outperforms the other peer DE algorithms in terms of both solution accuracy and convergence speed on most test functions.

A novel dipole antenna at X-band with wide-band operation is presented in this paper. A wire structure model with two centrosymmetric arms used for evolutionary design is proposed, which can be encoded using a vector of real-valued parameters for evolutionary algorithms to optimize. Differential evolution is used to solve the antenna problem, and the results we get show good performance: bandwidth larger than 4.5GHz and peak gain above 8dBi over the bandwidth. Compared with the antenna designed by a state-of-the-art conventional method we referenced, which has a bandwidth about 2.5GHz with peak gain around 7dBi, the evolved antenna shows significant improvements.

Maintaining population diversity is a crucial issue for the performance of evolutionary algorithms (EAs) in dynamic environments. In the literature of EAs for dynamic optimization problems (DOPs), many studies have been done to address this issue based on change detection techniques. However, many changes are hard or impractical to be detected in real-world applications. Although, some research has been done by means of maintaining diversity without change detection. These methods are not effective because the continuous focus on diversity slows down the optimization process. This paper presents a maintaining diversity method without change detection based on a clustering technique. The method was implemented through particle swarm optimization (PSO), which was named CPSOR. The performance of the CPSOR algorithm was evaluated on the GDBG benchmark. A comparison study with another algorithm based on change detection has shown the effectiveness of the CPSOR algorithm for tracking and locating the global optimum in dynamic environments.

The application of fluids in grinding is critical to the surface quality of the workpiece and the life of the grinding wheel. Lubrication and cooling are the most important roles provided by a grinding fluid. Hence, conventional method of flood delivering coolant fluid by a nozzle in order to achieve high process performance purposivelly. In the paper, effect of grinding fluid on grinding performances of vitrified bonded CBN wheel was analyzed and evaluated. Experiments were performed with precision cylindrical grinder M1420E and harded workpiece material 45 steel. To evaluate the influence of different cutting fluid, the experimental results such as microscopic topography (Scanning Electron Microscope images), surface roughness Ra values, and residual stress and diametrical wear amount of grinding wheel were investigated. The results showing that the surface quality and roughness Ra values of machined surface with lubricating oil were obviously improved comparing with emulsion fluid and synthetic fluid. The results revealed that the CBN wheel and grinding oil give an optimum combination for performance grinding operation.

In the real world, many optimization problems are dynamic. This requires an optimization algorithm to not only find the global optimal solution under a specific environment but also to track the trajectory of the changing optima over dynamic environments. To address this requirement, this paper investigates a clustering particle swarm optimizer (PSO) for dynamic optimization problems. This algorithm employs a hierarchical clustering method to locate and track multiple peaks. A fast local search method is also introduced to search optimal solutions in a promising subregion found by the clustering method. Experimental study is conducted based on the moving peaks benchmark to test the performance of the clustering PSO in comparison with several state-of-the-art algorithms from the literature. The experimental results show the efficiency of the clustering PSO for locating and tracking multiple optima in dynamic environments in comparison with other particle swarm optimization models based on the multiswarm method.

This article is posted here with permission of the IEEE - Copyright @ 2009 IEEE Traditional particle swarm optimization (PSO) suffers from the premature convergence problem, which usually results in PSO being trapped in local optima. This paper presents an adaptive learning PSO (ALPSO) based on a variant PSO learning strategy. In ALPSO, the learning mechanism of each particle is separated into three parts: its own historical best position, the closest neighbor and the global best one. By using this individual level adaptive technique, a particle can well guide its behavior of exploration and exploitation. A set of 21 test functions were used including un-rotated, rotated and composition functions to test the performance of ALPSO. From the comparison results over several variant PSO algorithms, ALPSO shows an outstanding performance on most test functions, especially the fast convergence characteristic. This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of the United Kingdom under Grant EP/E060722/1.

This article is posted here with permission of the IEEE - Copyright @ 2009 IEEE In the real world, many applications are nonstationary optimization problems. This requires that optimization algorithms need to not only find the global optimal solution but also track the trajectory of the changing global best solution in a dynamic environment. To achieve this, this paper proposes a clustering particle swarm optimizer (CPSO) for dynamic optimization problems. The algorithm employs hierarchical clustering method to track multiple peaks based on a nearest neighbor search strategy. A fast local search method is also proposed to find the near optimal solutions in a local promising region in the search space. Six test problems generated from a generalized dynamic benchmark generator (GDBG) are used to test the performance of the proposed algorithm. The numerical experimental results show the efficiency of the proposed algorithm for locating and tracking multiple optima in dynamic environments. This work was supported by the Engineering and Physical Sciences Research Council (EPSRC) of the United Kingdom under Grant EP/E060722/1.

In the paper, for many-objective optimization problems, the authors pointed out that the Pareto Optimality is unfair, unreasonable and imperfect for Many-objective Optimization Problems (MOPs) underlying the hypothesis that all objectives have equal importance and propose a new evolutionary decision theory. The key contribution is the discovery of the new definition of optimality called E-optimality for MOP that is based on a new conception, so called E-dominance, which not only considers the difference of the number of superior and inferior objectives between two feasible solutions, but also considers the values of improved objective functions underlying the hypothesis that all objectives in the problem have equal importance. Two new evolutionary algorithms for E-optimal solutions are proposed. Because the new relation

Particle swarm optimization (PSO) has shown its fast search speed in many complicated optimization and search problems. However, PSO could often easily fall into local optima. This paper presents an Opposition-based PSO (OPSO) to accelerate the convergence of PSO and avoid premature convergence. The proposed method employs opposition-based learning for each particle and applies a dynamic Cauchy mutation on the best particle. Experimental results on many well- known benchmark optimization problems have shown that OPSO could successfully deal with those difficult multimodal functions while maintaining fast search speed on those simple unimodal functions in the function optimization.

Particle swarm optimization (PSO) has shown its fast search speed in many complicated optimization and search problems. However, PSO could often easily fall into local optima because the particles could quickly get closer to the best particle. At such situations, the best particle could hardly be improved. This paper proposes a new hybrid PSO (HPSO) to solve this problem by adding a Cauchy mutation on the best particle so that the mutated best particle could lead all the rest of particles to the better positions. Experimental results on many well-known benchmark optimization problems have shown that HPSO could successfully deal with those difficult multimodal functions while maintaining fast search speed on those simple unimodal functions in the function optimization