An improved firefly algorithm for solving dynamic multidimensional knapsack problems

There is a wide range of publications reported in the literature, considering optimization problems where the entire problem related data remains stationary throughout optimization. However, most of the real-life problems have indeed a dynamic nature arising from the uncertainty of future events. Optimization in dynamic environments is a relatively new and hot research area and has attracted notable attention of the researchers in the past decade. Firefly Algorithm (FA), Genetic Algorithm (GA) and Differential Evolution (DE) have been widely used for static optimization problems, but the applications of those algorithms in dynamic environments are relatively lacking. In the present study, an effective FA introducing diversity with partial random restarts and with an adaptive move procedure is developed and proposed for solving dynamic multidimensional knapsack problems. To the best of our knowledge this paper constitutes the first study on the performance of FA on a dynamic combinatorial problem. In order to evaluate the performance of the proposed algorithm the same problem is also modeled and solved by GA, DE and original FA. Based on the computational results and convergence capabilities we concluded that improved FA is a very powerful algorithm for solving the multidimensional knapsack problems for both static and dynamic environments.     

Rapid optimization of blast wave mitigation strategies using Quiet Direct Simulation and Genetic Algorithm

Presented is a rapid calculation tool for the optimization of blast wave related mitigation strategies. The motion of gas resulting from a blast wave (specified by the user) is solved by the Quiet Direct Simulation (QDS) method - a rapid kinetic theory-based finite volume method. The optimization routine employed is a newly developed Genetic Algorithm (GA) which is demonstrated to be similar to a Differential Evolution (DE) scheme with several modifications. In any Genetic Algorithm, individuals contain genetic information which is passed on to newly created individuals in successive generations. The results from unsteady QDS simulations are used to determine the individual's “genetic fitness” which is employed by the proposed Genetic Algorithm during the reproduction process. The combined QDS/GA algorithm is applied to various test cases and finally the optimization of a non-trivial blast wave mitigation strategy. Both QDS and the proposed GA are demonstrated to perform with minimal computational expense while accurately solving the optimization problems presented.     

Multi-Criteria Job Scheduling in Grid Using an Accelerated Genetic Algorithm

The continuous growth of computation power requirement has provoked computational Grids, in order to resolve large scale problems. Job scheduling is a very important mechanism and a better scheduling scheme can greatly improve the efficiency of Grid computing. A lot of algorithms have been proposed to address the job scheduling problem. Unfortunately, most of them largely ignore the security risks involved in executing jobs in such an unreliable environment as Grid. This is known as security problem and it is a main hurdle to make the job scheduling secure, reliable and fault-tolerant. In this paper, we present a Genetic Algorithm with multi-criteria approach, in terms of job completion time and security risks. Although Genetic Algorithms are suitable for large search space problems such as job scheduling, they are too slow to be executed online. Hence, we changed the implementation of a traditional genetic algorithm, proposing the Accelerated Genetic Algorithm. We also present the Accelerated Genetic Algorithm with Overhead which concerns the extra overhead caused by the application of Accelerated Genetic Algorithm. Accelerated Genetic Algorithm and Accelerated Genetic Algorithm with Overhead are compared with three well-known heuristic algorithms. Simulation results indicate a substantial performance advantage of both Accelerated Genetic Algorithm and Accelerated Genetic Algorithm with Overhead.     

Human evolutionary model: A new approach to optimization

The aim of this paper is to propose the Human Evolutionary Model (HEM) as a novel computational method for solving search and optimization problems with single or multiple objectives. HEM is an intelligent evolutionary optimization method that uses consensus knowledge from experts with the aim of inferring the most suitable parameters to achieve the evolution in an intelligent way. HEM is able to handle experts’ knowledge disagreements by the use of a novel concept called Mediative Fuzzy Logic (MFL). The effectiveness of this computational method is demonstrated through several experiments that were performed using classical test functions as well as composite test functions. We are comparing our results against the results obtained with the Genetic Algorithm of the Matlab’s Toolbox, Evolution Strategy with Covariance Matrix Adaptation (CMA-ES), Particle Swarm Optimizer (PSO), Cooperative PSO (CPSO), G3 model with PCX crossover (G3-PCX), Differential Evolution (DE), and Comprehensive Learning PSO (CLPSO). The results obtained using HEM outperforms the results obtained using the abovementioned optimization methods.     

Augmented grasshopper optimization algorithm by differential evolution: a power scheduling application in smart homes

With the increasing number of electricity consumers, production, distribution, and consumption problems of produced energy have appeared. This paper proposed an optimization method to reduce the peak demand using smart grid capabilities. In the proposed method, a hybrid Grasshopper Optimization Algorithm (GOA) with the self-adaptive Differential Evolution (DE) is used, called HGOA. The proposed method takes advantage of the global and local search strategies from Differential Evolution and Grasshopper Optimization Algorithm. Experimental results are applied in two scenarios; the first scenario has universal inputs and several appliances. The second scenario has an expanded number of appliances. The results showed that the proposed method (HGOA) got better power scheduling arrangements and better performance than other comparative algorithms using the classical benchmark functions. Moreover, according to the computational time, it runs in constant execution time as the population is increased. The proposed method got 0.26?% enhancement compared to the other methods. Finally, we found that the proposed HGOA always got better results than the original method in the worst cases and the best cases.

     

A directional mutation operator for differential evolution algorithms

Differential evolution (DE) is widely studied in the past decade. In its mutation operator, the random variations are derived from the difference of two randomly selected different individuals. Difference vector plays an important role in evolution. It is observed that the best fitness found so far by DE cannot be improved in every generation. In this article, a directional mutation operator is proposed. It attempts to recognize good variation directions and increase the number of generations having fitness improvement. The idea is to construct a pool of difference vectors calculated when fitness is improved at a generation. The difference vector pool will guide the mutation search in the next generation once only. The directional mutation operator can be applied into any DE mutation strategy. The purpose is to speed up the convergence of DE and improve its performance. The proposed method is evaluated experimentally on CEC 2005 test set with dimension 30 and on CEC 2008 test set with dimensions 100 and 1000. It is demonstrated that the proposed method can result in a larger number of generations having fitness improvement than classic DE. It is combined with eleven DE algorithms as examples of how to combine with other algorithms. After its incorporation, the performance of most of these DE algorithms is significantly improved. Moreover, simulation results show that the directional mutation operator is helpful for balancing the exploration and exploitation capacity of the tested DE algorithms. Furthermore, the directional mutation operator modifications can save computational time compared to the original algorithms. The proposed approach is compared with the proximity based mutation operator as both are claimed to be applicable to any DE mutation strategy. The directional mutation operator is shown to be better than the proximity based mutation operator on the five variants in the DE family. Finally, the applications of two real world engineering optimization problems verify the usefulness of the proposed method.     

A continuous DC programming approach for resource allocation in OFDMA/TDD wireless networks

The next generation broadband wireless networks deploys OFDM/OFDMA as the enabling technologies for broadband data transmission with QoS capabilities. Many optimization problems have arisen in the conception of such a network. This article studies an optimization problem in resource allocation. By using mathematical modeling technique we formulate the considered problem as a pure integer linear program. This problem is reformulated as a DC (Difference of Convex functions) program via an exact penalty technique. We then propose a continuous approach for its resolution. Our approach is based on DC programming and DCA (DC Algorithm). It works in a continuous domain, but provides integer solutions. To check globality of computed solutions, a global method combining DCA with a well adapted Branch-and-Bound (B&B) algorithm is investigated. Preliminary numerical results are reported to show the efficiency of the proposed method with respect to the standard Branch-and-Bound algorithm.     

Using real-coded genetic algorithms for Weibull parameter estimation

Genetic algorithms (GAs) represent a class of adaptive search techniques based on a direct analogy to Darwinian natural selection and mutations in biological systems. “Standard” GAs have emphasized the utilization of binary codes. However, recent empirical results have indicated that a chromosome representation which utilizes real values have enhanced the performance of these GAs in certain engineering problems. A real-valued Genetic Algorithm method described in this paper estimates the parameter values from an unconstrained population of data points for a Weibull distribution function using a simultaneous random search function by integrating the principles of the Genetic Algorithm and the method of Maximum Likelihood Estimation. The results of the real-coded GA technique for parameter estimation are compared to the results of the Newton-Raphson Algorithm.     

Hybrid models based on biological approaches for speech recognition

This paper aims to adapt the Clonal Selection Algorithm (CSA) which is usually used to explain the basic features of artificial immune systems to the learning of Neural Networks, instead of Back Propagation. The CSA was first applied to a real world problem (IRIS database) then compared with an artificial immune network. CSA performance was contrasted with other versions of genetic algorithms such as: Differential Evolution (DE), Multiple Populations Genetic Algorithms (MPGA). The tested application in the simulation studies were IRIS (vegetal database) and TIMIT (phonetic database). The results obtained show that DE convergence speeds were faster than the ones of multiple population genetic algorithm and genetic algorithms, therefore DE algorithm seems to be a promising approach to engineering optimization problems. On the other hand, CSA demonstrated good performance at the level of pattern recognition, since the recognition rate was equal to 99.11% for IRIS database and 76.11% for TIMIT. Finally, the MPGA succeeded in generalizing all phonetic classes in a homogeneous way: 60% for the vowels and 63% for the fricatives, 68% for the plosives.     

基于遗传算法的数字罗盘误差补偿方法研究

提高地磁导航精度的关键在于地磁罗盘的误差补偿,本文通过分析数字罗盘误差产生的原因,提出一种基于遗传算法的误差补偿方法,通过遗传算法的交叉、变异、选择等过程,对罗盘误差补偿参数进行优化,算法克服了传统方法的局部最优问题,达到一种全局最优化。实验结果表明:该算法可以有效修正环境磁场误差,将平均误差由补偿前的10.81°降低到0.53°,补偿后的罗盘可以为地磁导航提供更为精确的航向信息。     

A comparative review on mobile robot path planning: Classical or meta-heuristic methods?

The involvement of Meta-heuristic algorithms in robot motion planning has attracted the attention of researchers in the robotics community due to the simplicity of the approaches and their effectiveness in the coordination of the agents. This study explores the implementation of many meta-heuristic algorithms, e.g. Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO) and Cuckoo Search Algorithm (CSA) in multiple motion planning scenarios. The study provides comparison between multiple meta-heuristic approaches against a set of well-known conventional motion planning and navigation techniques such as Dijkstra’s Algorithm (DA), Probabilistic Road Map (PRM), Rapidly Random Tree (RRT) and Potential Field (PF). Two experimental environments with difficult to manipulate layouts are used to examine the feasibility of the methods listed. several performance measures such as total travel time, number of collisions, travel distances, energy consumption and displacement errors are considered for assessing feasibility of the motion planning algorithms considered in the study. The results show the competitiveness of meta-heuristic approaches against conventional methods. Dijkstra ’s Algorithm (DA) is considered a benchmark solution and Constricted Particle Swarm Optimization (CPSO) is found performing better than other meta-heuristic approaches in unknown environments.     

Multi-mode renewable resource-constrained allocation in PERT networks

This paper presents a hybrid metaheuristic algorithm (HMA) for Multi-Mode Resource-Constrained Project Scheduling Problem (MRCPSP) in PERT networks. A PERT-type project, where activities require resources of various types with random duration, is considered. Each activity can be accomplished in one of several execution modes and each execution mode represents an alternative combination of resource requirements of the activity and its duration. The problem is to minimize the regular criterion namely project's makespan by obtaining an optimal schedule and also the amount of different resources assigned to each activity. The resource project scheduling model is strongly NP-hard, therefore a metaheuristic algorithm is suggested namely HMA. In order to validate the performance of new hybrid metaheuristic algorithm, solutions are compared with optimal solutions for small networks. Also the efficiency of the proposed algorithm, for real world problems, in terms of solution quality and CPU time, is compared to one of the well-known metaheuristic algorithms, namely Genetic Algorithm of Hartmann (GAH). The computational results reveal that the proposed method provides appropriate results for small networks and real world problems.     

EXPLICA: An Explorative Imperialist Competitive Algorithm based on the notion of Explorers with an expansive retention policy

Meta-heuristic algorithms are of considerable importance in solving optimization problems. This importance is more highlighted when the problems to be optimized are too complicated to achieve a solution using conventional methods or, the traditional methods are somehow not applicable for solving them. Imperial Competitive Algorithm has been proved to be an efficient and effective meta-heuristic optimization algorithm and it has been successfully applied in many scientific and engineering problems. By introducing the concept of explorers and retention policy, the original algorithm is enhanced with a dynamic population mechanism in this paper and hence, the performance of the Imperial Competitive Algorithm is improved. Performance of the proposed modification is tested with experiments of optimizing real-values functions and results are compared with results obtained with the original Imperialistic Competitive Algorithm, Genetic Algorithm, Particle Swarm Optimization and Simulated Annealing. Also, the applicability of the proposed improvement is verified by optimizing a ship propeller design problem.     

基于模拟退火的混合差分进化算法及其在联合补货-配送集成优化中的应用

针对差分进化 (Differential evolution, DE)算法搜索效率较低和容易陷入局部最优的缺点,设计了基于SA的混合差分进化算法(SA-based Hybrid DE, SAHDE),以提高DE算法的全局寻优能力。该算法采用自适应变异算子和交叉算子,并结合模拟退火(Simulated Annealing, SA)算法的Metropolis 准则。首先通过标准测试函数对改进的SAHDE进行性能测试,证明了该算法比DE、自适应混合DE (Adaptive Hybrid DE, AHDE)和遗传算法(Genetic Algorithm, GA)更有效。进而将该算法运用到联合补货-配送集成优化(典型NP-hard)问题的求解中,通过大规模的算例分析,证实SAHDE在解决联合补货-配送优化问题比DE、AHDE和GA更有效。     

Structural topology design optimization using Genetic Algorithms with a bit-array representation

In this paper, a bit-array representation method for structural topology optimization using the Genetic Algorithm (GA) is implemented. The importance of structural connectivity in a design is further emphasized by considering the total number of connected objects of each individual explicitly in an equality constraint function. To evaluate the constrained objective function, Deb’s constraint handling approach is further developed to ensure that feasible individuals are always better than infeasible ones in the population to improve the efficiency of the GA. A violation penalty method is proposed to drive the GA search towards the topologies with higher structural performance, less unusable material and fewer separate objects in the design domain. An identical initialization method is also proposed to improve the GA performance in dealing with problems with long narrow design domains. Numerical results of structural topology optimization problems of minimum weight and minimum compliance designs show the success of this bit-array representation method and suggest that the GA performance can be significantly improved by handling the design connectivity properly.     

FL-GrCCA: A granular computing classification algorithm based on fuzzy lattices

Defining a relation between granules and computing ever-changing granules are two important issues in granular computing. In view of this, this work proposes a partial order relation and lattice computing, respectively, for dealing with the aforementioned issues. A fuzzy lattice granular computing classification algorithm, or FL-GrCCA for short, is proposed here in the framework of fuzzy lattices. Algorithm FL-GrCCA computes a fuzzy inclusion relation between granules by using an inclusion measure function based on both a nonlinear positive valuation function, namely arctan, and an isomorphic mapping between lattices. Changeable classification granules are computed with a dilation operator using, conditionally, both the fuzzy inclusion relation between two granules and the size of a dilated granule. We compare the performance of FL-GrCCA with the performance of popular classification algorithms, including support vector machines (SVMs) and the fuzzy lattice reasoning (FLR) classifier, for a number of two-class problems and multi-class problems. Our computational experiments showed that FL-GrCCA can both speed up training and achieve comparable generalization performance.     

A Differential Covariance Matrix Adaptation Evolutionary Algorithm for real parameter optimization

Hybridization in context to Evolutionary Computation (EC) aims at combining the operators and methodologies from different EC paradigms to form a single algorithm that may enjoy a statistically superior performance on a wide variety of optimization problems. In this article we propose an efficient hybrid evolutionary algorithm that embeds the difference vector-based mutation scheme, the crossover and the selection strategy of Differential Evolution (DE) into another recently developed global optimization algorithm known as Covariance Matrix Adaptation Evolutionary Strategy (CMA-ES). CMA-ES is a stochastic method for real parameter (continuous domain) optimization of non-linear, non-convex functions. The algorithm includes adaptation of covariance matrix which is basically an alternative method of traditional Quasi-Newton method for optimization based on gradient method. The hybrid algorithm, referred by us as Differential Covariance Matrix Adaptation Evolutionary Algorithm (DCMA-EA), turns out to possess a better blending of the explorative and exploitative behaviors as compared to the original DE and original CMA-ES, through empirical simulations. Though CMA-ES has emerged itself as a very efficient global optimizer, its performance deteriorates when it comes to dealing with complicated fitness landscapes, especially landscapes associated with noisy, hybrid composition functions and many real world optimization problems. In order to improve the overall performance of CMA-ES, the mutation, crossover and selection operators of DE have been incorporated into CMA-ES to synthesize the hybrid algorithm DCMA-EA. We compare DCMA-EA with original DE and CMA-EA, two best known DE-variants: SaDE and JADE, and two state-of-the-art real optimizers: IPOP-CMA-ES (Restart Covariance Matrix Adaptation Evolution Strategy with increasing population size) and DMS-PSO (Dynamic Multi Swarm Particle Swarm Optimization) over a test-suite of 20 shifted, rotated, and compositional benchmark functions and also two engineering optimization problems. Our comparative study indicates that although the hybridization scheme does not impose any serious burden on DCMA-EA in terms of number of Function Evaluations (FEs), DCMA-EA still enjoys a statistically superior performance over most of the tested benchmarks and especially over the multi-modal, rotated, and compositional ones in comparison to the other algorithms considered here.     

应用于铣削参数优化的粒子群和遗传交互算法

针对工程领域中的非线性、多极值和多维度等复杂优化问题,提出把遗传算子引入粒子群算法中,采用粒子搜索变异,交互学习的方法。方法综合了粒子群算法原理简单、搜索速度快,遗传算法全局搜索能力强的特点,实现了算法避免陷入局部最优解,以获得较高的精度和执行力。通过对比分析,此交互学习策略在求解精度、效率和处理多种复杂度问题等方面都有优越性,特别适用于精确求解和解决复杂优化问题。实例证明,算法可以解决基于机械动力学理论的铣削参数优化中非线性、多极值、多维度的工程问题。     

A hybrid Differential Evolution—Tabu Search algorithm for the solution of Job-Shop Scheduling Problems

The Job-Shop Scheduling Problem (JSSP) has drawn considerable interest during the last decades, mainly because of its combinatorial characteristics, which make it very difficult to solve. The good performances attained by local search procedures, and especially Nowicki and Smutnicki's i-TSAB algorithm, encouraged researchers to combine such local search engines with global methods. Differential Evolution (DE) is an Evolutionary Algorithm that has been found to be particularly efficient for continuous optimization, but which does not usually perform well when applied to permutation problems. We introduce in this paper the idea of hybridizing DE with Tabu Search (TS) in order to solve the JSSP. A competitive neighborhood is included within the TS with the aim of determining if DE is able to replace the re-start features that constitute the main strengths of i-TSAB (i.e., a long-term memory and a path-relinking procedure). The computational experiments reported for more than 100 JSSP instances show that the proposed hybrid DE–TS algorithm is competitive with respect to other state-of-the-art techniques, although, there is still room for improvement if the adequacy between the solution representation modes within DE and TS is properly stressed.     

Automatic multi-thresholds selection for image segmentation based on evolutionary approach

Automatic thresholding has been widely used in machine vision for automatic image segmentation. Otsu’s method selects an optimum threshold by maximizing the between-class variance in a grayscale image. However, the method becomes time-consuming when extended to multi-level threshold problems, because excessive iterations are required in order to compute the cumulative probability and the mean of class. In this paper, we focus on the issue of automatic selection for multi-level thresholding, and we greatly improve the efficiency of Otsu’s method for image segmentation based on evolutionary approaches. We have investigated and evaluated the performance of the Otsu and Valleyemphasis thresholding methods. Based on our evaluation results, we have developed many different algorithms for automatic threshold selection based on the evolutionary method using the Modified Adaptive Genetic Algorithm and the Hill Climbing Algorithm. The experimental results show that the evolutionary approach achieves a satisfactory segmentation effect and that the processing time can be greatly reduced when the number of thresholds increases.