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According to Genetic algorithms principle, the new hybrid evolutionary algorithm (HEA) is proposed in this paper by combining the Immune algorithm, Genetic algorithm and Pareto optimal solutions. The HEA has high convergence precision and improved the diversity of population. Multiple near optimization paths can be developed by the algorithm with multi‐objective restriction, and satisfy to minimize the routing of transportation and the numbers of the vehicles. The HEA has been used to solve the vehicle routing problem, the results of simulation experiment show that the HEA can gain higher global convergence rate and higher speed.

How to generate the task-oriented optimal agent coalition is a key issue of multi-agent system, which is a typical optimization problem. In this paper, an improved particle swarm optimization (IPSO) is proposed to solve this problem. In order to overcome the premature and local optimization problem in traditional particle swarm optimization (PSO), we proposed a variation of inertia weight PSO algorithm by analyzing the feasibility of particle optimization process in PSO. Compared with several well-known algorithms such as PSO, ACO, experimental results show that the global search capability of IPSO has been significantly improved and IPSO can effectively avoid premature convergence problem. Also it can solve the multi-agent coalition formation problem effectively and efficiently.

본 연구에서는 GeoGebra를 활용한 부등식 영역의 최대 최소 문제의 지도 과정에서 나타나는 학생들의 등고선 개념에 대한 이해 수준의 특징을 분석하였다. 또한 부등식 영역의 최대・최소 학습 지도에서 GeoGebra의 활용이 학생들의 수학적 사고에 미치는 영향과 함께 교실 수업에서 GeoGebra의 활용을 위한 시사점을 알아보았다. 연구의 결과로 첫째, 연구대상 학생들 중에서 주희는 등고선 개념에 대해서 과정 수준, 혜민이와 다영이는 행동 수준의 특성을 보였다. 둘째, 부등식 영역의 최대・최소 지도에서 GeoGebra의 활용은 학생들의 등고선 개념의 이해 수준을 향상시킬 수 있었다. 또한 GeoGebra의 활용을 통해 학생들은 목적함수의 의미를 파악할 수 있었으며, 목적함수식에서의 변수적 측면을 이해하고, 최댓값과 최솟값이 경계에서만 나오는 이유를 알 수 있었다.

This study was intended to suggest realistic knowledge for learning and teaching the optimization problems in regional inequalities using GeoGebra. For this purpose, learning material that is adequate to apply GeoGebra was developed and applied to experimental teaching. At first, through reviewing previous studies about learning of the optimization problems in regional inequalities, it is found that the concept of level curve is essential in studying the content. So analysis was conducted for the concept of level curve and development of the concept based on APOS theory that explains the development of understanding mathematical concept as the construction of mental structures: action, process, object. From the reviewing cases of utilizing GeoGebra for math class, availability of certain functions of GeoGebra like coordinate indicating function, drawing graph, dragging graph, and slider emerged as suitable teaching methods to understand the level curve. As a result, learning materials of activities with GeoGebra was designed so that student's understanding of the level curve can be improved. Secondly, the effects of using GeoGebra in teaching optimization problems in regional inequalities were investigated by analyzing the feature of learning・teaching through the developed learning materials. The analysis was conducted focusing on the change in level of student's understanding of the level curve. The followings are the results of this study. Through the concrete activities with GeoGebra, students' were able to understand the meaning of level curve, the variability of k in the expression f(x,y)=k, and relation between k and level curve f(x,y)=k. During the class with GeoGebra activities, students could correct their misconception and understand the reason why the maximum and minimum values are assumed only on the boundary of the area of solution of inequalities.

With the rapid development of computer network, the problem of reliability of computer network is becoming more and more attention of builders, users and the network designer. The reliability of computer network has become a key technical indicators to measure the comprehensive performance of computer network. Aiming at a single known computer network, applying to established the computer network link cost model and the computer network reliability model, and carries on the simulation using the genetic algorithm intelligent algorithm; comprehensive evaluation for a variety of known different computer network reliability index system, and we have given the evaluation results. The simulation results show that, the established computer network link cost model and the computer network reliability model in the thesis are suitable, model algorithm and programming language is effective, it has the practical application significance which the paper proposed the computer network reliability optimization design concrete plan.

The successful implementation of remanufacturing supply chain not only needs foundation engineering technology, but also needs the efficient supply chain model to support logistics network. But till now, the optimization problems of supply chain logistics network focus on the determination for the number and location of facilities and logistics distribution between the various facilities. Except for the decisive factors, the optimization problem of remanufacturing supply chain logistics network considers the environmental pollution factors the waste product returns and transportation. The market demand and waste product returns are actually uncertain in remanufacturing supply chain. There is few papers focus on dual uncertain factors although there are lots of studies on the problems. Therefore, based on dual stochastic programming, the optimization model of multi-phase multi-objective remanufacturing supply chain is established with maximum profit in the remanufacturing supply chain, maximum rapid response customer satisfaction and minimum environmental pollution. Dual-layer genetic algorithm mechanism was brought up. The first layer algorithm is responsible for supply chain logistics network structure. The second layer algorithm determines specific distributions for remanufacturing supply chain, based on optimized logistics network structure mechanism in the first layer algorithm. Finally, numerical examples demonstrate the validity of the model and algorithm for the optimization problem.

Genetic algorithms for multi-objective optimization problem to be solved were studied. Through the elitist strategy analysis, it is an improved multi-objective optimization algorithm. The algorithm uses a data warehouse to store the optimal solution produced by individuals in each generation, from the way individuals adopt measures to phase out the individual data warehouse identical or similar, the algorithm also improved selection operator, so that the algorithm adaptive capacity enhancement, the new algorithm improves the algorithm performance, improves the quality of understanding between sets, can get a lot of optimal and balanced.

In the present work, an investigation has been made to study the effect of cutting parameters (speed, feed and depth of cut) on the multi-responses, Material Removal Rate (MRR) and Surface Roughness (Ra). The experiments were done on a conventional lathe using a tungsten carbide tool under dry environment. Twenty seven alternative combinations of speed, feed and depth of cut were considered (as per Taguchi’s standard L27 Orthogonal Array) for the machining of medium carbon steel EN8. For the optimization of multi-responses, the Multi-criteria decision making (MCDM) method, TOPSIS has been employed. Main effect plot for the Signal-to-Noise (S/N) ratios of the relative closeness coefficient (Ci+) was drawn using the MINITAB-16 software. From the TOPSIS and the main effect plot drawn for the relative closeness coefficient (Ci+), the optimal combination of the multi-responses were found at 27th alternative i.e. speed at 760 rpm, feed at 0.3 mm/rev and depth of cut at 1.5 mm respectively. The analysis of variance (ANOVA) has been done to know the influence of cutting parameters on the multi-responses. From the ANOVA results of the relative closeness coefficient (Ci+), it is found that the feed has high influence (F =41.08) followed by the depth of cut (F =25.78) and speed (F = 22.55).

This article is based on the mixture of tabu search algorithm and interior search algorithm(ISA) to address the reliability redundancy assignment problem. Iinterior search algorithm is immersed in a tabu search algorithm. TS is used to search solution space and ISA is applied to generate neighborhood solutions. The merit of two algorithms is considered at the same time. And a mixed TS-ISA method is proposed to deal with three benchmark reliability redundancy optimization problem. The experimental results show that a the method is effective and efficient for RRAP by comparing with other results in the previous literaturs.

A global optimal algorithm derived from electromagnetism-like mechanism (EM), called as enhanced searching electromagnetism-like (ESEM) algorithm, was developed in this paper. The original EM is a meta-heuristic algorithm utilizing an attraction-repulsion mechanism (called as force F) to move sample points towards optimality in global optimization problems. Compared to the original algorithm, the best historical visited positions of each point were added into the search process in the improved algorithm. In ESEM, the search direction and step length of points were determined together by its best previously visited position, best point in current swarm and total force F. Preliminary experiments showed that additional best historical positions can help to improve the convergence property. More importantly, the improved searching mechanism can effectively solve the problem of stagnation of the original algorithm caused by too small values of force F.

In order to improve the weak situation of the global search ability, the stability and time consuming of optimization of differential evolution(DE) algorithm in solving high dimensional optimization problem, an improved differential evolution algorithm with multi- population and multi-strategy(MPMSIDE) is proposed to solve high dimensional optimization problem. Firstly, the different DE mutation strategies are studied. Then the MPMSIDE algorithm divides the population into several sub-populations, which evolve independently and communicate with each other at regular intervals by using different DE strategies, in order to save the computation time. And the improved mutation strategy and local optimization strategy are introduced to raise and balance the global searching ability and local searching ability, and improve the optimization efficiency. The selfadaptive update strategy is used to adjust the scaling factor and crossover factor for making the parameter sensitivity of DE algorithm and improving the stability and robustness. Finally, the proposed MPMSIDE algorithm is applied to standard test function optimization for verifying the effectiveness. The experimental results show that the proposed MPMSIDE algorithm has a relatively better optimization performance for solving complex optimization problem, and takes on remarkable optimizing ability, higher searching accuracy and faster convergence speed.

In order to improve the global searching ability of differential evolution algorithm in solving complex optimization problem, an improved differential evolution (SMDE) algorithm based on the self-adaptive method and multi-population is proposed in this paper. In the proposed SMDE algorithm, the population is divided into multi-populations in order to keep the diversity, then the self-adaptive method is used to control the parameters of differential evolution algorithm in order to balance the local search and global search ability. Finally, several complex benchmark functions are selected to validate the efficiency of the SMDE algorithm. The experiment results show that the proposed SMDE algorithm is better at the global convergence ability and the searching precision.

Particle swarm optimization (PSO) algorithm is a population-based search algorithm by simulating the social behavior of birds within a flock. It is a simple and efficient optimization algorithm. But it exists the low computational speed and easy falling into local optimal solution in solving the complex problem. So the quantum theory, adaptive inertia weight, disturbance factor and diversity mutation strategy are introduced into the PSO algorithm in order to propose an improved PSO(IWDMDQPSO) algorithm in this paper. In the IWDMDQPSO algorithm, the quantum theory is used to change the updating mode of the particles for guaranteeing the simplification and effectiveness of the algorithm. The adaptive inertia weight is used to improve the premature convergence of the algorithm. The disturbance factor is used to avoid the premature of the algorithm. The diversity mutation strategy is used to improve the global searching ability and computation speed. Finally, the famous benchmark functions are selected to prove the performance and effectiveness of the proposed IWDMDQPSO algorithm. The experiment results show that the proposed IWDMDQPSO algorithm takes on better solving accuracy and higher computation speed in solving the complex function. So it has a remarkable optimization performance.

Without multi query optimization, Relational Database Management System for online and analytical decision support systems would have been inefficient and hence unpractical. It is an expensive process because it relies at a great extent on evaluating the different plans (access paths) and choosing an optimal one among them. In Multi Query Optimization, queries are executed in batches and there were many different algorithms acted in such way that, in case some queries have a common sub-expression such a sub- expression is executed once and the output shared. We studied the basic multi query optimization algorithms including Basic Volcano, Volcano-SH and Volcano RU, identified their strengths and weaknesses and recommend strategies for developing new improved multi query optimization algorithm so as to reduce weaknesses and integrate strengths of the different basic multi query algorithms into one efficient algorithm.

The combination of traditional ant colony algorithm in solving the optimization process to consume a large amount of time, easily falling into local optimal solution and convergence is slow and other disadvantages, while also generating a lot of useless redundant iterative code, operation efficiency is low. Therefore, ant colony optimization algorithm is proposed. The algorithm based on genetic algorithm has the ability to search the global ant colony algorithm also has a parallel and positive feedback mechanisms. Changes in the use of genetic algorithm selection operator, crossover operator and mutation operator action to determine the distribution of pheromone on the path, the ant colony algorithm for feature selection using support vector machine classifiers for evaluating the performance characteristics of the feedback sub-Variorum And by changing the pheromone iteration, parameter selection and increase the local pheromone update feature nodes guided the re-combination. The algorithm uses probability expectation values are obtained to meet under the conditions with minimal sensor nodes, and gives the optimal coverage and connectivity probability models and reasoning. The experimental results show that, the algorithm can not only use the least nodes complete the effective target area to be covered, and in reducing the network energy consumption is also greatly improved, simultaneously reduces the cyber source configuration, improve the network life cycle.

This paper presents a hybrid algorithm for traveling salesman problem. The algorithm is a combination of the genetic algorithm and simulated annealing algorithm; in other words, it is a hybrid algorithm. The combination overcomes the deficiencies of the two algorithms when acting separately. The real distance between customers has been used on the basis of geographical information system (GIS) in order to make the result more suitable in real-life. The algorithm has tested on the examples of international standards. We made a comparison with the result of second nearest neighbor algorithm and genetic optimization algorithm. The test showed that the algorithm proposed in this paper has improved the results.

This research proposed an application of swarm inspired new meta heuristic algorithm Grey Wolf Optimization to solve active power dispatch problem imposing valve point effect and generator constraints. Grey Wolf optimization is based on mathematical approach whose solution convergence inspired by the leadership hierarchy and hunting mechanism of grey wolves. It explores search space as a multi-level decision mechanism and does not require gradient for search path. This approach converged to global optimal solution in spite of the non linearity added by valve point effect while solving the fitness function. Optimal scheduling of generators to minimize the total operating cost coupled with generator constraints and valve point effect to match load demand is implemented with proposed method and. Exploration, Computation and Convergence power are evaluated to track the computational efficiency of the proposed technique. The presented technique is tested on different test cases comprises three, six and thirteen test systems incorporating valve point effect. Test results are compared with other nature and bio inspired algorithms presented in literature .Analysis shows cut throat results as total operating cost turns out to be minimum as compared to other techniques which infers the effectiveness of proposed method and encourage to further explore the potential of proposed method to solve complex optimization problems in active power dispatch planning area.

With the growing number of alternative services in the cloud environment, users have put forward new requirements to solve the service dynamic selection problem quickly and efficiently. In this paper, an evaluation model of service process which considers concurrent requests and service association is proposed. This model evaluates the service process from three dimensions which are functional quality dimension, non-functional quality dimension and transactional dimension. To solve the service selection problem efficiently, we first design a novel coding strategy of particle, and then propose an approach based on hybrid particle swarm optimization algorithm which combines the crossover and mutation operators of genetic algorithm. The experimental results show that our proposed approach is feasible and effective.

In order to improve the search result and low evolution speed, and avoid the tendency towards stagnation and falling into the local optimum of ant colony optimization(ACO) in solving the complex function, the traditional ant colony optimization algorithm is analyzed in detail, an improved ant colony optimization(IWSMACO) algorithm based on information weight factor and supervisory mechanism is proposed in this paper. In the proposed IWSMACO algorithm, the information weight factor is added to the path selection and pheromone adjustment mechanisms in order to dynamically adjust path selection probability and randomly select the behavior rules for further intelligentializing the ant colony. The supervisory mechanism added the dynamic convergence criterion of supervisory distance and used the optimal pheromone update strategy to self-adaptively select the excellent ants for updating the pheromone trails, and improve the solution qualities of each iteration, better guide the later ants for learning. Finally, the proposed IWSMACO algorithm is carried out by 12 TSP instances. The simulation experiment results show that the proposed IWSMACO algorithm can not only avoid falling into the local optimum, but also enhance the convergence speed. And it takes on remarkable optimized ability and higher search accuracy.

This paper presents the use of a novel evolutionary algorithm called Biogeography-based optimization (BBO) for the solution of the optimal power flow problem. The objective is to minimize the total fuel cost of generation and environmental pollution caused by fossil based thermal generating units and also maintain an acceptable system performance in terms of limits on generator real and reactive power outputs, bus voltages, shunt capacitors/reactors and power flow of transmission lines. BBO searches for the global optimum mainly through two steps: Migration and Mutation. In the present work, BBO has been applied to solve the optimal power flow problems on IEEE 30-bus test system with six generating units to test the effectiveness of the proposed method. Satisfactory results obtained from the proposed method were compared to conventional and evolutionary optimization methods.