Earticle

This work proposes the utilization of the quantum-inspired evolutionary algorithm (QEA) for user pairing in non-orthogonal multiple access (NOMA). By exploiting quantum concepts such as superposition, it obtained a user pairing solution that approximates the highest achievable sum rate. Moreover, elitist QEA (E-QEA) is proposed to further enhance the performance by eliminating the risk of losing the best solution of the current iteration in the next iteration. Simulation result demonstrates that E-QEA and QEA yield higher average achievable sum rates compared to random user pairing.

The efficient deployment of robot-based manufacturing systems is frequently hindered by the substantial time required for programming collision-free robot paths during the commissioning process. This challenge involves intensive tasks such as teach-in, offline programming, and subsequent path optimization. To dramatically accelerate this critical stage, the industry needs an automatic and intelligent path planning system. This work introduces a novel system designed for the autonomous path planning of industrial robots. We conduct an explicit comparison between samplingbased methods such as probabilistic roadmaps (PRM) and rapidly exploring random Trees (RRT), and computational intelligence (CI) based methods, particularly genetic algorithms. Our findings demonstrate the potential for these advanced techniques to drastically reduce robot deployment time.

The importance of eco-friendly architectural design has become an essential design element along with the global climate crisis. Nevertheless, eco-friendly considerations are generally made in the mid to late stages of the architectural design process, and in particular, there is a high tendency to focus on consideration of architectural facilities or facade design rather than determining the overall shape, such as the layout, height, or orientation of the building. However, as mentioned above, the decisions made in the early design process often determine the actual performance of the building, so more advanced research is needed on ways to increase the energy performance of buildings in the early design stage. An algorithm was designed so that the eco-friendly design techniques derived through this study can be universally applied not only to specific sites but also to unspecified sites. Through the two proposed stages - placement, height and orientation - it was possible to derive an optimized building shape in the early design stage, and thus it can be said to be a technique that enables a high level of energy savings throughout the entire life cycle of the building.

One of the most important and effort intensive activity of the entire software development process is software testing. The effort involved chiefly increases because of the need to obtain optimal test data out of the entire search space of the problem under testing. Software test data generation is one area that has seen tremendous research in terms of automation and optimization. Generating or identifying an optimal test set that satisfies a more robust adequacy criteria, like data flow testing, is still a challenging task. A number of heuristic and meta-heuristics like genetic algorithm (GA), Particle Swarm Optimization (PSO) have been applied to optimize the test data generation problem. GA, although more popular, has its own difficulties such as complex to implement and slow convergence rate. In this paper an Adaptive Particle Swarm Optimization (APSO) algorithm is applied to generate test data for data-flow dependencies of a program guided by a novel fitness function. Adaptive PSO is used because of its capability of balancing in exploration and exploitation. A new fitness function is designed based on the concepts of dominance relations, weighted branch distance for APSO to guide the search direction. A set of benchmark programs and four modules of Krishna Institute of Engineering and Technology (KIET), Enterprise resource planning (ERP) system were taken for the experimental analysis. The experimental results show that the proposed adaptive PSO based approach performed significantly better than random search, Genetic Algorithm and PSO in enhancing the convergence speed.

Harmony search algorithm (HSA) is an evolutionary algorithm which is used to solve a wide class of problems. HSA is based on the idea of musician's behavior in searching for better harmonies. It tries to find the optimal solution according to an objective function. HSA has been applied to various optimization problems such as timetabling, text summarization, flood model calibration. In this paper we used HSA to select an optimal subset of features that gives a better accuracy results in solving the face recognition problem. The proposed approach is compared with the standard Principal Component Analysis (PCA). A set of images that each has a face adopted from the literature is used to evaluate the proposed algorithm. The obtained results show that using HSA to select the subset of features gives better accuracy in face recognition.

Preview Control is a field well suited for application to systems that have reference signals known a priori. The use of advance knowledge of reference signal can improve the tracking quality of the concerned control system. The classical solution to the Preview Control problem is obtained using the Algebraic Riccati Equation. The solution obtained is good but it is not optimal and has a scope of improvement, as the Preview Control problem has many parameters to be defined and optimized. The Evolutionary Algorithms, inspired by real-time natural systems, are a solution to this multi-dimensional problem. This paper studies the performance of Preview Control optimized by three Evolutionary Algorithms (Genetic Algorithm, Particle Swarm Optimization and Marriage in Honey Bees Optimization) for two bench-mark control systems: Industrial Servo and Inverted Pendulum System in terms of processing time, convergence characteristics and the quality of solution obtained. The results of this paper illustrate the benefits and weaknesses of the Evolutionary Algorithms for solving the Preview Control problem. The PSO algorithm proves to be the best for Preview Control based problems. It performs well in both small and large search spaces. The study reveals that the MBO algorithm requires more computation time while the performance of GA degrades with the increase in number of parameters to be tuned.

In this study, a multi-input multi-output (MIMO) semi-active fuzzy control algorithm has been developed for vibration control of a seismically excited building structure. The MIMO fuzzy controller was optimized by evolutionary genetic algorithm. For numerical simulation, a five-story example building structure is used and two MR dampers are employed as semi-active control devices. For comparison purpose, a clipped-optimal control strategy based on acceleration feedback is employed for controlling MR dampers to reduce structural responses due to seismic loads. Numerical simulation results show that the MIMO fuzzy control algorithm can provide superior control performance to the clipped-optimal control algorithm. When the design method proposed in this study is applied, Pareto optimal solutions can be obtained in single optimization run. Therefore, an alternative solution can be easily selected by an engineer.

Genetic programming (GP) is an automated method for creating a working computer program from a high-level problem statement of a problem. Genetic programming starts from a high-level statement of “what needs to be done” and automatically creates a computer program to solve the problem. In artificial intelligence, genetic programming (GP) is an evolutionary algorithm-based methodology inspired by biological evolution to find computer programs that perform a user defined task. It is a specialization of genetic algorithms (GA) where each individual is a computer program. It is a machine learning technique used to optimize a population of computer programs according to a fitness span determined by a program's ability to perform a given computational task. This paper presents a idea of the various principles of genetic programming which includes, relative effectiveness of mutation, crossover, breeding computer programs and fitness test in genetic programming. The literature of traditional genetic algorithms contains related studies, but through GP, it saves time by freeing the human from having to design complex algorithms. Not only designing the algorithms but creating ones that give optimal solutions than traditional counterparts in noteworthy ways.

This article deals with solving the flexible job shop scheduling problem in dynamic environment (DFJSSP). In this problem, environment may face with many real time events such as random arrival of jobs or breakdown machine efficiency. Jobs and their operations are processing the machines according to static scheduling in which environment might face with such events. Regarding being NP – hard of the problem , a hybrid of artificial immune and virus evolutionary algorithm are offered to solve it which use the technique of stable action – reaction scheduling . In these algorithms two objective functions are minimized: Efficiency and stability. Efficiency is the objectives value in static scheduling, whereas stability is presented in dynamic scheduling because its purpose is to improve static scheduling, reduce the deviation from the first scheduling, and increase system stability.

The paper provides an improved evolutionary strategy (ES) of genetic algorithm (GA) on the basis of the existing literature. The ES overcomes the shortage of traditional GA whose excellent child individuals obtained in the crossover process may not survive in the process of mutation. In addition, the crossover probability and mutation probability which is hard to determine in traditional GA is removed for this proposed strategy. At the same time, it increases the number of individuals produced in process of crossover. This may increase the possibility of producing excellent individuals, thus lead to better improvement of the traditional GA. The test result of finding the optimal values of four functions using transitional GA and the proposed GA is presented in this paper. The result shows that the improved ES presented in this paper has faster calculation speed and significantly smaller number of iterations than the traditional GA. Thus, the improvement of improved ES is powerfully illustrated. Based on articles in the existing research literature, the initial population generation methods were further explored when using the genetic algorithm(GA) for solving constrained optimization problem. Through the research we present a new method about initial interior point’s generation. Firstly, construct a constraint posed by the objective function, which is based on the characteristics of constrained optimization problems. Then translate the problem of evaluating the initial interior point into a problem of solving a series of unconstrained optimization. By solving the unconstrained optimization problem, we achieve the solution of the initial interior point. Based on this idea, the research has given a method on the generation of the rest initial population individuals. In addition, through the research we concluded that the key to generate the initial population is to obtain an initial point. The production of other individuals will take less time after the initial internal point is obtained. Finally, we verified by examples that the initial population generation method given by this paper is a fast and reliable method. Thus the shortage of the GA of which the initial population is difficult to be produced in some constrained optimization problem is overcome

Parallel genetic algorithms are particularly easy to implement and promise substantial gains in performance. Its basic idea is to keep several subpopulations that are processed by genetic algorithms. Furthermore, a migration mechanism produces a chromosome exchange between subpopulation. In this paper, a new selection method based on non-linear fitness assignment presented. The use of proposed ranking selection permits higher local exploitation search, where the diversity of populations is structure. Experimental results show that the relation between local-global search balance and the probabilities of reaching a desired solution.

Simple Genetic Algorithm(SGA) proposed by J. H. Holland is a population-based optimization method based on the principle of the Darwinian natural selection. The theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. Although GA does well in many applications as an optimization method, still it does not guarantee the convergence to a global optimum in some problems. In designing intelligent systems, specially, since there is no deterministic solution, a heuristic trial-and error procedure is usually used to determine the systems' parameters. As an alternative scheme, therefore, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve. In this paper we review the existing co-evolutionary algorithms and propose co-evolutionary schemes designing intelligent systems according to the relation between the system's components.

최적화 방법이 구조설계 분야에 사용된지 근30년이 지난 오늘 이론적 측면에서 보면 상당한 발전이 있어 왔다고 해도 과언이 아니다. 실무 입장에 볼 때 과연 얼마만큼 최적화 방법이 현실의 설계업무 속에 자리잡혀 있는가를 곰곰히 되새겨 볼 필요가 있다고 본다. 사실 실제와 이론 사이의 괴리를 줄여보려는 노력에서, 최적호 기술 분야에서도 기존의 확정론적 최적화 방법만이 아니라 확률론적 최적화방법에 대한 연구도 시작되었으리라 본다. 본문에서 언급한 Genetic Algorithm과 Evolutionary Strategy도 기존의 최적화 방법과 마찬가지 이유에서 복잡한 현실문제의 최적해를 추구하기 위한 또 하나의 방법으로 인식 될 필요가 있다고 보아 이 두 방법에 대한 개략적인 내용을 적었다.

Advanced planning and scheduling (APS) refers to a manufacturing management process by which raw materials and production capacity are optimally allocated to meet demand. APS is especially well-suited to environments where simpler planning methods cannot adequately address complex trade-offs between competing priorities. However, most scheduling problems of APS in the real world face both inevitable constraints such as due date, capability, transportation cost, set up cost and available resources. In this survey paper, we address three crucial issues in APS, including basic scheduling model, job-shop scheduling (JSP), assembly line balancing (ALB) model, and integrated scheduling models for manufacturing and logistics. Several evolutionary algorithms which adapt to the problems are surveyed and proposed; some test instances based on the practical problems demonstrate the effectiveness and efficiency of evolutionary approaches.

A new co-evolutionary algorithm, of which the convergence speed is accelerated by neural networks, is proposed and verified in this paper. To reduce computational load required for co-evolutionary optimization processes, the cost function and constraint information is stored in the neural networks, and the extra offspring group, whose cost is computed by the neural networks, is generated. It increases the offspring population size without overloading computational effort; therefore, the convergence speed is accelerated. The proposed algorithm is applied to attitude control design of flexible satellites, and it is verified by computer simulations and experiments using a torque-free air bearing system.

Simple Genetic Algorithm(SGA) proposed by J. H. Holland is a population-based optimization method based on the principle of the Darwinian natural selection. The theoretical foundations of GA are the Schema Theorem and the Building Block Hypothesis. Although GA goes well in many applications as an optimization method, still it does not guarantee the convergence to a global optimum in some problems. In designing intelligent systems, specially, since there is no deterministic solution, a heuristic trial-and error procedure is usually used to determine the systems' parameters. As an alternative scheme, therefore, there is a growing interest in a co-evolutionary system, where two populations constantly interact and co-evolve. In this paper we review the existing co-evolutionary algorithms and propose co-evolutionary schemes designing intelligent systems according to the relation between the system's components.

This paper focuses on three very similar evolutionary algorithms: genetic algorithm (GA), particle swarm optimization (PSO), and differential evolution (DE). While GA is more suitable for discrete optimization, PSO and DE are more natural for continuous optimization. The paper first gives a brief introduction to the three EA techniques to highlight the common computational procedures. The general observations on the similarities and differences among the three algorithms based on computational steps are discussed, contrasting the basic performances of algorithms. Summary of relevant literatures is given on job shop, flexible job shop, vehicle routing, location-allocation, and multimode resource constrained project scheduling problems.

The performance of a distributed database system depends particularly on the site-allocation of the fragments. Queries access different fragments among the sites, and an originating site exists for each query. A data allocation algorithm should distribute the fragments to minimize the transfer and settlement costs of executing the query plans. The primary cost for a data allocation algorithm is the cost of the data transmission across the network. The data allocation problem in a distributed database is NP-complete, and scalable evolutionary algorithms were developed to minimize the execution costs of the query plans. In this paper, quadratic assignment problem heuristics were designed and implemented for the data allocation problem. The proposed algorithms find near-optimal solutions for the data allocation problem. In addition to the fast ant colony, robust tabu search, and genetic algorithm solutions to this problem, we propose a fast and scalable hybrid genetic multi-start tabu search algorithm that outperforms the other well-known heuristics in terms of execution time and solution quality.

진화 연산의 확률적 모델인 베이지안 진화 알고리즘이 개체군의 크기를 1로 제한하고 고정된 차원의 탐색 공간을 갖는 경우, 목표 확률분포에 수렴함이 이전 연구[2]를 통해 증명되었다. 본 논문에서는 개체군의 크기가 2 이상인 경우의 베이지안 진화 알고리즘을 개체군 자체를 하나의 상태로 보는 단일 체인의 베이지안 입자 필터(particle filter)로 변환하여, 입자 필터의 수렴 특성을 이용하여 목표 확률분포에 수렴함을 증명한다.