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In this paper, a new optimization method based on a combination of Differential Evolution (DE) and Evolution Strategy (ES), which belong to both Meta-Heuristics and Evolutionary Computation, are developed as a fast approximation optimization method. A weak point of DE is weak local search ability and considerable computation time for obtaining a good approximate solution. The proposed method aims...
Clustering is one of the most commonly data explorer techniques in Data Mining. K-harmonic means clustering (KHM) is an extension of K-means (KM) and solves the problem of KM initialization using a built-in boosting function. However, it is also suffering from running into local optima. As a stochastic global optimization technique, harmony search (HS) can solve this problem. HS-based KHM, HSKHM not...
This paper presents a modified harmony search (MHS) algorithm for solving 0-1 knapsack problems. MHS employs position update strategy for generating new solution vectors that enhances accuracy and convergence rate of harmony search (HS) algorithm. Besides, the harmony memory consideration rate (HMCR) is dynamically adapted to the changing of objective function value in the current harmony memory....
A well-known problem faced by Multi-Objective Particle Swarm Optimization Algorithms (MOPSO) is the deterioration of its search ability when the number of objectives scales up. In the literature some techniques were proposed to overcome these limitations, however, most of them focuses on alternatives to the non-domination relation. In this work, a different direction is explored, and some specific...
A major drawback of evolutionary optimization approaches in the literature is the apparent lack of automated knowledge transfers and reuse across problems. Particularly, evolutionary optimization methods generally start a search from scratch or ground zero state, independent of how similar the given new problem of interest is to those optimized previously. In this paper, we present a study on the...
The automated generation of feature pattern based if-then rules is essential to the success of many intelligent pattern classifiers, especially when their inference results are expected to be directly human-comprehensible. Fuzzy and rough set theories have been applied with much success to this area as well as to feature selection. Both applications involve the use of equivalence classes for a successful...
In this study, the performance of Differential Evolution with landscape modality detection and a diversity archive (LMDEa) is reported on the set of benchmark functions provided for the CEC2012 Special Session on Large Scale Global Optimization. In Differential Evolution (DE), large population size, which is much larger than the number of decision variables in problem to be solved, is adopted in order...
In this paper, a bi-dimensional search method is proposed for unconstrained optimization problems. Traditional methods for solving the unconstrained optimization problems are usually based on one-dimensional search (or line search) to determine the step size as these methods only use one descent direction in each iteration. When two search directions are available in each iteration, conventional one-dimensional...
In the fields of computer vision, multiple object tracking is an active research area. It is a challenging problem mainly due to the frequent occlusions and interactions that happen between the multiple targets. We formulate the multiple interaction problem as an optimization problem and explore Particle Swarm Optimization (PSO) algorithm for the optimal solution. To tackle the problem of premature...
Differential Evolution (DE) is an evolutionary algorithm. DE has been successfully applied to optimization problems including non-linear, non-differentiable, non-convex and multimodal functions. There are several mutation strategies such as the best and the rand strategy in DE. It is known that the best strategy is suitable for unimodal problems and the rand strategy is suitable for multimodal problems...
Differential Evolution (DE) is a simple and effective approach for solving numerical optimization problems. However, the performance of DE is sensitive to the choice of the mutation and crossover strategies and their associated control parameters. Therefore, to obtain optimal performance, time consuming parameter tuning is necessary. In DE, different mutation and crossover strategies with different...
This paper presents the addition of an adaptive stepsize value and a local search operator to the modified bacterial foraging algorithm (MBFOA) to solve constrained optimization problems. The adaptive stepsize is used in the chemotactic loop for each bacterium to promote a suitable sampling of solutions and the local search operator aims to promote a better trade-off between exploration and exploitation...
This paper presents a novel multi-swarm sharing management for differential evolution (MsSDE) to deal with numerical optimization effectively. Multi-swarm is an effective search concept to keep the original search characteristic or effective balance strategies. However, it still has some defects need to overcome, such as weak search ability for smaller swarm and easy to fall into local optimal position...
Differential evolution (DE) is undoubtedly one of the most powerful stochastic searching optimization algorithms. However, solving a specific problem using DE crucially depends on appropriately choosing of trial vector generation strategies and their associated control parameters. At the same time, multimodal optimization refers to locating not only one optimum but a set of optimal solutions. Niching...
Many real-life optimization problems are dynamic in time, demanding optimization algorithms to perform search for the best solutions in a time-varying problem space. Among population-based Evolutionary Algorithms (EAs), Differential Evolution (DE) is a simple but highly effective method that has been successfully applied to a wide variety of problems. We propose a technique to solve dynamic optimization...
In this paper, an improved Differential Evolution (DE) with a self-adaptive strategy to determine the control parameters is proposed to solve constrained real-parameter optimization, combined with the dynamic constraint-handling mechanism. It is implemented by restating the single-objective constrained optimization as a set of single-objective unconstrained problems and dynamically assigning to the...
In this paper, the interest is on cases where assessing the goodness of a solution for the problem is costly or hazardous to construct or extremely computationally intensive to compute. We label such category of problems as “expensive” in the present study. In the context of multi-objective evolutionary optimizations, the challenge amplifies, since multiple criteria assessments, each defined by an...
In this paper, the Hunting Search Algorithm is proposed for solving the transmission-constrained unit commitment problem. First, the algorithm, which is an evolutionary optimization method, is used for solving the main unit commitment problem, and then the solution is modified for satisfying the lines congestion constraints, using the linear programming method. Moreover, the units ramp rate constraints...
A local search strategy for static transmission expansion planning (TEP) is presented in this paper. Removing one circuit in current transmission plan may make some components overloaded, some buses isolated or the remained system intact. Based on the three situations, three sets known as local bus, local circuit and effective local circuit are defined, and accordingly the neighborhood of current...
This paper addresses the problem of coordinating a team of mobile autonomous sensor agents performing a cooperative mission while explicitly avoiding inter-agent collisions in a team negotiation process. Many multi-agent cooperative approaches disregard the potential hazards between agents, which are an important aspect to many systems and especially for airborne systems. In this work, team negotiation...
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