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This work presents an optimization method combined with evolutionary space search algorithm (ESSA) for solving numerical optimization problems. The main strategy of the ESSA is to divide the feasible solution space into many subspaces and search for the solution by finding the optimal subspace. To facilitate the global exploration property, the subspace is characterized in terms of quantum bit representation...
Particle swarm optimization (PSO) algorithm is widely used in function optimization, but the performance of PSO algorithm is restricted as frequently occurrences of the premature. Introduce immune operator to PSO algorithm can be to avoid premature and improve the performance of algorithm. For the four benchmark functions, the results show that the immune operator improve performance of algorithm...
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...
This paper presents fusion of Bacterial Foraging with parameter free Particle Swarm Optimization (HBF-pfPSO). The proposed technique is used to enhance quality of global optima of multimodal functions. The authors propose two major modifications in Bacterial Foraging Optimization (BFO). Firstly, all bacteria position and direction are updated after all fitness evaluations instead of each fitness evaluation...
Biogeography-Based Optimization (BBO) is a new bio-inspired and population based optimization algorithm. The convergence of original BBO to the optimum value is slow. Intelligent Biogeography-Based Optimization (IBBO) technique is a hybrid version of BBO with Bacterial Foraging algorithm (BFA). In this paper, authors integrate the bacterial intelligence feature of BFA to decide the valid emigration...
Most optimization problems have constraints of different types (e.g., physical, time, geometric, etc.), which modify the shape of the search space. We propose an ecologically inspired invasive weed optimization (IWO) algorithm to solve the constrained real-parameter optimization problems. Central to our approach is a parameter-free penalty function that we introduce. The adaptive nature of the penalty...
A new optimization technique named as sliced particle swarm optimization (SPSO) is proposed. It introduces the slicing of search space into rectangular slices. It gives complete solution in terms of reduction in the computational cost and tracking minutely each sliced search space. It introduces the momentum factor which restricts the particle in a sliced search space. Linearly decreasing inertia...
Extinction-based Evolutionary Algorithms (EEA) have been recently developed as the solutions for the problem of early convergence in multimodal optimization tasks. The reproduction of EEAs is done only by mutation. Moreover, according to recent studies, several attempts have been made to prove rigorously that crossover is essential for typical optimization problems. The results of these researches...
This paper presents an empirical analysis of the performance of differential evolution (DE) variants on different classes of unconstrained global optimization benchmark problems. This analysis has been undertaken to identify competitive DE variants which perform reasonably well on a range of problems with different features. Towards this, fourteen DE variants were implemented and tested on 14 high...
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...
This paper presents a new diversity guided particle swarm optimization algorithm (PSO) named beta mutation PSO or BMPSO for solving global optimization problems. The BMPSO algorithm makes use of an evolutionary programming based mutation operator to maintain the level of diversity in the swarm population, thereby maintaining a good balance between the exploration and exploitation phenomena and preventing...
In the present study a Modified Differential Evolution (MDE) algorithm is proposed. This algorithm is different in three ways from basic DE. For initialization it utilizes opposition-based learning while in basic DE uniform random numbers serve this task. Secondly, in basic DE mutant individual is random while in MDE it is tournament best and finally MDE utilizes only one set of population as against...
As an effective tool for optimization, differential evolution (DE) has aroused much interest. But the premature convergence of it often gives rise to erroneous results so should be improved. In this paper, a novel differential evolutionary algorithm (DECH) based on chaos local search (CLS) is proposed, which divides DE algorithm into two stages. Firstly, DECH runs with original DE model 'DE/best/1/bin'...
In this paper, a chaotic cooperative particle swarm optimization based on tent map (TCCPSO) is proposed. The cooperative particle swarm optimization (CPSO), can significantly improve the performance of the original algorithm. However, CPSO has the defect of leading to pseudominimizer, which can not be easily escaped by interleaving the CPSO and PSO algorithm. Therefore, we take full advantages of...
Cellular Learning Automata (CLA) is one of the newest optimization methods for solving NP-hard problems. The Job Shop Scheduling Problem (JSSP) is one of these problems. This paper, proposes a new approach for solving the JSSP using CLA with two kinds of actions' set. By generating actions based on received responses from the problem environment, appropriate position for operations of jobs is chosen...
We propose a common set of validation benchmarks and a reference algorithm that provide ground-truth for comparative analysis of multi-source robot localization algorithms for large scale applications. The benchmarks capture the primary first-order attributes of the general problem: source characterization and distribution, initial robot distributions, and dead space. The biased random walk (BRW)...
Hybridization is a useful method to enhance the performance of particle swarm optimizer (PSO). In this paper, a novel particle swarm optimizer (NHPSO) combining PSO with a constriction factor (CF-PSO) and the fully informed particle swarm optimizer (FIPSO) in cycles is proposed, in order to balance the convergence speed and search accuracy. Six most commonly used benchmarks are used to evaluate the...
This paper presents a performance study of two versions of a unidimensional search algorithm aimed at solving high-dimensional optimization problems. The algorithms were tested on 11 scalable benchmark problems. The aim is to observe how metaheuristics for continuous optimization problems respond with increasing dimension. To this end, we report the algorithms' performance on the 50, 100, 200 and...
A method for optimization of continuous nonlinear functions is introduced. Seed Throwing Optimization is a probabilistic metaheuristic. It has roots in hill climbing and the evolutionary computation like technique harmony search. The relationship to these algorithms is shown in this paper. Our method is tested in a benchmark and compared to other metaheuristics. Seed Throwing Optimization is a randomized...
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