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Aiming at the permutation flow-shop scheduling problem (PFSSP) with makespan criterion, a combination algorithm based on differential evolution (DE) and estimation of distribution algorithm (EDA), namely DE-EDA, is proposed. Firstly, DE-EDA combines the probability-dependent macro information extracted by EDA and the individual-dependent micro information obtained by DE to execute the exploration,...
This paper presents a differential evolution optimized fuzzy clustering algorithm (DEOFCA), which combines differential evolution (DE) algorithm and fuzzy clustering theory. Since DE algorithm has strong global search ability and good robustness, DEOFCA uses DE to replace the iteration process of fuzzy C means clustering algorithm, by which the global optimization capability is greatly improved. An...
Data fusion approaches are nowadays needed and also a challenge in many areas, like sensor systems monitoring complex processes. This paper explores evolutionary computation approaches to sensor fusion based on unsupervised nonlinear transformations between the original sensor space (possibly highly-dimensional) and lower dimensional spaces. Domain-independent implicit and explicit transformations...
In the present study we propose a new hybrid version of differential evolution (DE) and particle swarm optimization (PSO) algorithms. In the proposed algorithm named as hybrid differential evolution (HDE) a `switchover constant' called ?? is defined. HDE starts as the basic DE algorithm which switches over to PSO when ?? is activated. The constant ?? on the other hand is activated at a point where...
Evolutionary Algorithms are inspired by biological and sociological motivations and can take care of optimality on rough, discontinuous and multimodal surfaces. During the last few decades, these algorithms have been successfully applied for solving numerical bench mark problems and real life problems. This paper presents the application of two popular Evolutionary Algorithms (EA); namely Particle...
This paper is focusing on extracting chain codes of a thinned binary image using two soft computing approaches, i.e. Differential Evolution (DE) and Particle Swarm Optimization (PSO). The problem is to find a continuous route which covers all of the nodes of the image. The motivation is that finding such a route is complicated when it has many branches. Literature review shows that these approaches...
This paper presents a novel variant of particle swarm optimization (PSO) called adaptive accelerated exploration particle swarm optimizer (AAEPSO). AAEPSO algorithm identifies the particles which are far away from the goal and accelerate them towards goal with an exploration power. These strategies particularly avoid the premature convergence and improve the quality of solution. The performance comparisons...
Differential evolution (DE) algorithm is a heuristic approach that gains more interest in today's research. It finds the true global minimum regardless of the initial parameter values, fast convergence, and using few control parameters. DE algorithm is a population based algorithm like genetic algorithm using similar operators; crossover, mutation and selection. This paper addresses the restrictive...
Many real world problems which can be assigned to the machine learning domain are inverse problems. The available data is often noisy and may contain outliers, which requires the application of global optimization. Evolutionary Algorithms (EA's) are one class of possible global optimization methods for solving such problems. Within population based EA's, Differential Evolution (DE) is a widely used...
Reduction of Single Input Single Output (SISO) discrete systems into Reduced Order Model (ROM), using a conventional and a bio-inspired evolutionary technique is presented in this paper. In the conventional technique, mixed advantages of Modified Cauer Form (MCF) and differentiation are used. In this method, the original discrete system is first converted into equivalent continuous system by applying...
An improved differential evolution algorithm is given. In the algorithm a logarithm increased crossover and a random migration operator are used to overcome the convergent slowness in the later period of the iteration and fall easily into premature convergence. It is shown by the experiments on eight typical problems that the modified algorithm has strongly global search ability.
Differential evolution (DE) is a kind of simple but powerful evolutionary optimization algorithm with many successful applications. This paper proposed a multiobjective differential evolutionary algorithm based on opposite operation. Firstly, in the initialization of the algorithm, the opposite points of randomly generated individuals are calculated in order to make the initial population better....
In this paper we present an empirical, comparative performance, analysis of fourteen variants of Differential Evolution (DE) and Dynamic Differential Evolution (DDE) algorithms to solve unconstrained global optimization problems. The aim is to compare DDE, which employs a dynamic evolution mechanism, against DE and to identify the competitive variants which perform reasonably well on problems with...
Differential evolution (DE) is a simple and efficient scheme for global optimization over continuous spaces. DE is generally considered as a reliable, accurate, robust and fast optimization techniques. It outperforms many other optimization algorithms in terms of convergence speed and robustness over common benchmark problems and real world applications. However, the user is required to set the values...
The bacterial foraging optimization (BFO) algorithm is a nature and biologically inspired computing method. We propose an alternative solution integrating bacterial foraging optimization algorithm and tabu search (TS) algorithm namely TS-BFO. We modify the original BFO via established a self-control multi-length chemotactic step mechanism, and introduce rao metric. We utilize it to solve motif discovery...
This study addresses the solution of jobshop scheduling problems using differential evolution (DE). The issue of representing permutations through real numbers constitutes the key issue for developing an efficient implementation. Several techniques are empirically validated on problem instances traditionally adopted in the specialized literature. We also present a simple hybridization of DE with tabu...
According to the relationship of coordinated interaction between unit output and electricity price, an economic/risk/environmental generation optimal model for maximizing total profits in the dealing day and minimizing both risk and emissions was formulated in this paper. A new multi-objective differential evolution optimization algorithm, which integrated Pareto non-dominant sorting and differential...
Scheduling is one of the core steps to efficiently exploit the capabilities of heterogeneous distributed computing systems and it is also an appealing NP-complete problem. There is a number of heuristic and meta-heuristic algorithms that were tailored to deal with scheduling of independent jobs. In this paper we investigate the efficiency of differential evolution on the scheduling problem.
Multiple sequence alignment is one of the most important and challenging problem in bioinformatics, which is known as NP-hard problem. In this paper, a chaos-differential evolution (CDE) is proposed to solve MSA. In the proposed CDE algorithm, DE and chaos are hybridized to combine the evolutionary searching ability of DE and overcoming local optima of chaotic local search. Simulation shows that novel...
In this paper a scalability test over eleven scalable benchmark functions, provided by the current workshop (Evolutionary Algorithms and other Metaheuristics for Continuous Optimization Problems-A Scalability Test), are conducted for accelerated DE using generalized opposition-based learning (GODE). The average error of the best individual in the population has been reported for dimensions 50, 100,...
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