Multi-Robot Box-Pushing Using Non-dominated Sorting Bee Colony Optimization Algorithm

Rakshit, Pratyusha; Sadhu, Arup Kumar; Bhattacharjee, Preetha; Konar, Amit; Janarthanan, R.

doi:10.1007/978-3-642-27172-4_71

Cited by 10 publications

(5 citation statements)

References 5 publications

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“…As a matter of fact, many realworld scientific and engineering problems involve multiple conflicting performance measures or objectives, which must be optimized simultaneously to achieve a tradeoff among these different objectives. To solve such MOPs, some stochastic search and heuristic algorithms have been adopted, such as genetic algorithm (GA) [1]- [5], particle swarm optimization (PSO) [6]- [8], ant colony optimization (ACO) [9]- [11], differential evolution (DE) [12]- [15] and artificial bee colony (ABC) [16] [17].…”

Section: Introductionmentioning

confidence: 99%

Ranking Vectors by Means of the Dominance Degree Matrix

Zhou

Chen

Zhang

2017

IEEE Trans. Evol. Computat.

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In MOEAs, there are varieties of vector ranking schemes, including non-dominated sorting, dominance counting and so on. Usually, these vector ranking schemes in the classical MOEAs are of high computational complexity. Thus, in recent years, many researchers put emphasis on the further improvement of the computational complexity of the vector ranking schemes. In this paper, we propose the dominance degree matrix for a set of vectors and design a fast method to construct this new data structure, which requires O(mN log N ) comparisons on average. The dominance degree matrix is constructed based on the properties of Pareto domination, and it can convert the dominance comparison into counting the number of special element pairs. Based on the dominance degree matrix, we develop a new and efficient implementation of non-dominated sorting called DDA-NS, which has an average time complexity of O(mN 2 ) but only requires O(mN log N ) objective function value comparisons on average. Empirical results demonstrate that DDA-NS clearly outperforms six other representative approaches for non-dominated sorting in most cases and DDA-NS performs well when dealing with large-size and many-objective populations. In addition, we also use the dominance degree matrix to form a new method for calculating the dominance strength for SPEA2, which greatly improves the efficiency of the naive calculation method in SPEA2. Experiments on benchmark problems show that the NSGA-II and NSGA-III framework embedding DDA-NS and the SPEA2 framework embedding the new method of calculating the dominance strength indeed achieve the improvement of the runtime.

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Section: Introductionmentioning

confidence: 99%

Ranking Vectors by Means of the Dominance Degree Matrix

Zhou

Chen

Zhang

2017

IEEE Trans. Evol. Computat.

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“…One of the most popular members of the MOO family is Non-dominated Sorting Bee Colony (NSBC) (Rakshit et al 2011). It is inspired by the foraging behavior of bees.…”

Section: Non-dominated Sorting Bee Colonymentioning

confidence: 99%

“…Any traditional MOO algorithms, such as Non-dominated Sorting Bee Colony (NSBC) (Rakshit et al 2011), Differential Evolution for Multi-objective Optimization (DEMO) (Robic and Philipic 2005), Multi-objective Particle Swarm Optimization (MOPSO) (Coello and Lechuga 2002) and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) (Deb et al 2000), can be extended by the proposed approach in the present context. However, here we have selected NSBC for its simplicity in coding, fewer control parameters, good accuracy and fast speed of convergence.…”

Section: Introductionmentioning

confidence: 98%

“…Comparative framework: The MOO algorithms used for the comparative study include Differential Evolution for Multi-objective Optimization with Noise (DEMON) (Rakshit et al 2014), NSGA-II with α-dominance operator (NSGA-II-A) (Boonma and Suzuki 2009), elitist Evolutionary Multi-Agent System (elEMAS) (Siwik and Natanek 2008), extended MOPSO (Rakshit et al 2014), extended NSGA-II (Rakshit et al 2014), modified NSGA-II (Babbar et al 2003), Noise-Tolerant Strength Pareto Evolutionary Algorithm (NT-SPEA) (Buche et al 2002), DEMO-RSF-TS (Das et al 2005; Rakshit et al 2014) and traditional NSBC(Rakshit et al 2011). Selection of the above noisy/traditional MOO algorithms is justified because of their wide popularity in solving numerical MOO problems(Robic and Philipic 2005;Coello and Lechuga 2002).…”

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confidence: 98%

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Non-dominated Sorting Bee Colony optimization in the presence of noise

Rakshit

Konar

2015

Soft Comput

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The paper incorporates new extensional strategies into the traditional multi-objective optimization algorithms to proficiently obtain the Pareto-optimal solutions in the presence of noise in the fitness landscapes. The first strategy, referred to as adaptive selection of sample size, is employed to assess the trade-off between accuracy in fitness estimation and the associated run-time complexity. The second strategy is concerned with determining statistical expectation of fitness samples, instead of their conventional averaging, as the fitness measure of the trial solutions. The third strategy aims at improving Goldberg's approach to examine possible accommodation of a seemingly inferior solution in the optimal Pareto front using a more statistically viable comparator. The traditional Non-dominated Sorting Bee Colony algorithm has been ameliorated by extending its selection step with the proposed strategies. Experiments undertaken to study the performance of the proposed algorithm reveal that the extended algorithm outperforms its contenders with respect to four performance metrics, when examined on a test suite of 23 standard benchmarks with additive noise of three statistical distributions.

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“…Overview The Non-dominated Sorting Bee Colony (NSBC) algorithm is a popular algorithm inspired by the foraging behavior of bees [17]. NSBC encodes a solution using the position of a food source and its nectar amount (i.e., the fitness of the solution).…”

Section: Non-dominated Sorting Bee Colony Optimizationmentioning

confidence: 99%

Artificial Bee Colony Optimization to Reallocate Personnel to Tasks Improving Workplace Safety

Lazzerini

Pistolesi

2017

Lecture Notes in Computer Science

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Worldwide, just under 5,800 people go to work every day and do not return because they die on the job. The groundbreaking Industry 4.0 paradigm includes innovative approaches to improve the safety in the workplace, but Small and Medium Enterprises (SMEs)-which represent 99% of the companies in the EU-are often unprepared to the high costs for safety. A cost-effective way to improve the level of safety in SMEs may be just to reassign employees to tasks, so as to assign hazardous tasks to the more cautious employees. This paper presents a multi-objective approach to reallocate the personnel of a company to the tasks in order to maximize the workplace safety, while minimizing the cost, and the time to learn the new tasks assigned. Pareto-optimal reallocations are first generated using the Non-dominated Sorting artificial Bee Colony (NSBC) algorithm, and the best one is then selected using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The approach was tested in two SMEs with 11 and 25 employees, respectively.

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Multi-Robot Box-Pushing Using Non-dominated Sorting Bee Colony Optimization Algorithm

Cited by 10 publications

References 5 publications

Ranking Vectors by Means of the Dominance Degree Matrix

Ranking Vectors by Means of the Dominance Degree Matrix

Non-dominated Sorting Bee Colony optimization in the presence of noise

Artificial Bee Colony Optimization to Reallocate Personnel to Tasks Improving Workplace Safety

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