A hybrid algorithm based on particle swarm and chemical reaction optimization

Nguyen, Tien Trong; Li, Zhiyong; Zhang, Shiwen; Truong, Tung Khac

doi:10.1016/j.eswa.2013.09.012

Cited by 67 publications

(40 citation statements)

References 15 publications

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“…It has emerged as a new algorithm executing very efficiently in solving single-object optimization [17,19,32] with local search efficiency and not really efficient in global search. This point has been proven in [25].…”

Section: Introductionmentioning

confidence: 79%

A hybrid algorithm based on particle swarm and chemical reaction optimization for multi-object problems

Nguyen

Chen

et al. 2015

Applied Soft Computing

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Section: Introductionmentioning

confidence: 79%

A hybrid algorithm based on particle swarm and chemical reaction optimization for multi-object problems

Nguyen

Chen

et al. 2015

Applied Soft Computing

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“…Benchmark functions and Parameters: The benchmark functions in this paper are similar to the previous CRO publication [13][14][15][16], all experiments are simulated to solve the 23 objective problem functions. Such benchmark functions are classifi ed into three categories as shown in Table 1.…”

Section: With the Same Concept The Aim Of Maximizing Operation Is Tomentioning

confidence: 99%

“…Such benchmark functions are classifi ed into three categories as shown in Table 1. Category I is the high-dimensional unimodal functions, category II is the High-Dimensional Multimodal Functions, and category III is the Low-Dimensional Multimodal Functions, More details are contrasted in [13][14][15][16].…”

Section: With the Same Concept The Aim Of Maximizing Operation Is Tomentioning

confidence: 99%

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A Study of Global Numerical Maximization using Hybrid Chemical Reaction Algorithms

Havyarimana¹

2017

Trends Comput Sci Inf Technol

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Several approaches are proposed to solve global numerical optimization problems. Most of researchers have experimented the robustness of their algorithms by generating the result based on minimization aspect. In this paper, we focus on maximization problems by using several hybrid chemical reaction optimization algorithms including orthogonal chemical reaction optimization (OCRO), hybrid algorithm based on particle swarm and chemical reaction optimization (HP-CRO), real-coded chemical reaction optimization (RCCRO) and hybrid mutation chemical reaction optimization algorithm (MCRO), which showed success in minimization. The aim of this paper is to demonstrate that the approaches inspired by chemical reaction optimization are not only limited to minimization, but also are suitable for maximization. Moreover, experiment comparison related to other maximization algorithms is presented and discussed.

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“…Both simulation and real-life experiments proved that the HCRO algorithm task scheduling was much better than the existing algorithms in terms of makespan and speed of convergence. Another hybrid was proposed in [23], by combining the two local search operators in the CRO with global search ability for global optimum, incorporating concepts from the CRO and the Particle Swarm Optimization (PSO). Tests on a set of twenty three benchmark functions have shown that this CRO and PSO hybrid could outperform the CRO in most of the experiments.…”

Section: Literature Reviewmentioning

confidence: 99%

Maximally Distant Codes Allocation Using Chemical Reaction Optimization with Enhanced Exploration

Eldos¹,

Khreishah²

2016

ijacsa

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Abstract-Error correcting codes, also known as error controlling codes, are sets of codes with redundancy that provides for error detection and correction, for fault tolerant operations like data transmission over noisy channels or data retention using storage media with possible physical defects. The challenge is to find a set of m codes out of 2 n available n-bit combinations, such that the aggregate hamming distance among those codewords and/or the minimum distance is maximized. Due to the prohibitively large solution spaces of practically sized problems, greedy algorithms are used to generate quick and dirty solutions. However, modern evolutionary search techniques like genetic algorithms, swarm particles, gravitational search, and others, offer more feasible solutions, yielding near optimal solutions in exchange for some computational time. The Chemical Reaction Optimization (CRO), which is inspired by the molecular reactions towards a minimal energy state, emerged recently as an efficient optimization technique. However, like the other techniques, its internal dynamics are hard to control towards convergence, yielding poor performance in many situations. In this research, we proposed an enhanced exploration strategy to overcome this problem, and compared it with the standard threshold based exploration strategy in solving the maximally distant codes allocation problem. Test results showed that the enhancement provided better performance on most metrics.

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A hybrid algorithm based on particle swarm and chemical reaction optimization

Cited by 67 publications

References 15 publications

A hybrid algorithm based on particle swarm and chemical reaction optimization for multi-object problems

A hybrid algorithm based on particle swarm and chemical reaction optimization for multi-object problems

A Study of Global Numerical Maximization using Hybrid Chemical Reaction Algorithms

Maximally Distant Codes Allocation Using Chemical Reaction Optimization with Enhanced Exploration

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