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The objective of this paper is to propose a new parallelization approach of Particle Swarm Optimization (PSO). PSO is an intelligent optimization algorithm based on swarm intelligence, it is not only simple, easy to implement but also can be used to solve various complex problems that are difficult by using traditional calculation methods. In this work, a novel approach is presented for avoiding premature...
Artificial Bee Colony (ABC) algorithm has been applied to many scientific and engineering problems for its efficiency. However, the original ABC technique cannot be used in dynamic environments directly. This paper proposes a multiswarm ABC algorithm, or MABC, that has a similar framework as the original ABC but uses environment detecting technique to track the moving of the optimum of dynamic problems...
Shuffled Frog-Leaping Algorithm (SFLA) is a memetic meta-heuristic used for solving various combinatorial optimization problems. SFLA divides population into several memeplexes and then apply evolutionary process to update every memeplex. Like other evolutionary algorithm, it may also suffer from the problem of slow convergence. To improve the convergence and exploitation capability of SFLA, Binomial...
This paper compares performance of the artificial bee colony algorithm (ABC) and the real coded genetic algorithm (RCGA) on a suite of 9 standard benchmark problems. The problem suite comprises a diverse set of unimodal, multimodal and rotated multimodal numerical optimization functions and the comparison criteria include (i) solution quality, (ii) convergence speed, (iii) robustness, and (iv) scalability...
Artificial Bee Colony (ABC) algorithm is a swarm-based optimization algorithm with advantages like simplicity and proper exploration ability. However, it suffers from improper exploitation in solving complicated problems. In order to overcome this disadvantage, modifications on all three bee types are proposed. By introducing a new procedure for the scout bees and modifying the search patterns of...
New advancements in Meta-heuristics have forced the researchers to modify the existing algorithms in order to make them widely applicable to a large pool of complex problem set. Firefly Algorithm being a new nature-inspired algorithm has been used extensively for solving various optimization problems. The standard version namely, Standard Firefly Algorithm(SFA) was introduced in 2008 which uses the...
This paper presents a modified fruit fly optimization algorithm(FOA). The proposed modified FOA establishes a balanced tradeoff between exploration and exploitation, and thus overcomes original FOA's drawbacks of premature convergence and easy trapping in a local optima. In the proposed modified FOA, firstly, the whole population performs a global search; Secondly, the whole population are sequenced...
A quantum binary shuffled frog leaping algorithm (QBSFLA) is proposed through integrating the theories of the quantum evolutionary algorithm and the shuffled frog leaping algorithm (SFLA). Firstly, the superposition state characteristic of quantum makes the separate individual expresses more states, and the probability expression characteristic makes individuals' states are expressed with certain...
Artificial bee colony (ABC) is a new optimization technique which has shown to be competitive with some well-known evolutionary algorithms. However, ABC is good at exploration but poor at exploitation. Inspired by JADE (adaptive differential evolution with optional external archive), this paper proposes an improved ABC (IABC) algorithm with an external archive, which stores some best solutions during...
This paper proposes a modified marriage in honey-bee optimization for solving multiobjective optimization problems. Unlike the original marriage in honey-bee optimization, the proposed algorithm divides the objective space into several colonies, each of which has its own queen. The fitness of each solution is based on 3 parameters: the size of the colony, the number of dominating solutions, and the...
An improved artificial bee colony algorithm (IABC) is proposed in this paper, which is aimed at some defects of ABC such as low optimization precision and easy to fall into local optimal value. A new method is introduced to produce a new solution when the bees are going on neighborhood search in the improved algorithm. IABC is as simple as ABC to implement, but can greatly improve the ability of seeking...
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