Cockroach Swarm Optimization Algorithm for Travel Planning

Kwiecień, Joanna; Pasieka, Marek

doi:10.3390/e19050213

Cited by 21 publications

(13 citation statements)

References 36 publications

(38 reference statements)

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“…For example, in [ 29 ] we offer approaches to deliver ways of movements for the adaptation of the cockroach swarm optimization algorithm to the traveling salesman problem including crossover operators known from genetic algorithms and a 2-opt move. In turn, in [ 30 ] a procedure of individuals’ relocation had to be also developed to resolve the time-expanded graph in journey planning. As was mentioned in previous section, the observation of spatial navigation capability in various mazes can provide a basis for better monitoring the vehicles or robots rout.…”

Section: Swarm Movements To Generate Challenging Mazesmentioning

confidence: 99%

A Swarm-Based Approach to Generate Challenging Mazes

Kwiecień

2018

Entropy

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Swarm intelligence draws its inspiration from the collective behaviour of many individual agents interacting with both one another and their environment. This paper presents a possibility to apply a swarm-based algorithm, modelled after the behaviour of individuals operating within a group where individuals move around in the manner intended to avoid mutual collisions, to create the most challenging maze developed on a board with determined dimensions. When solving such a problem, two complexity measures are used. Firstly, the complexity of the path was assumed to be a quality criterion, depending on the number of bends and the length of the path between two set points that was subjected to maximisation. Secondly, we focus on the well-known concept of the maze complexity given as the total complexity of the path and all branches. Owing to the uniqueness of the problem, consisting in the maze modification, a methodology was developed to make it possible for the individuals belonging to their population to make various types of movements, e.g., approach the best individual, within the range of visibility, or relocate randomly. The test results presented here indicate a potential prospect of application of the swarm-based methods to generate more and more challenging two-dimensional mazes.

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Section: Swarm Movements To Generate Challenging Mazesmentioning

confidence: 99%

A Swarm-Based Approach to Generate Challenging Mazes

Kwiecień

2018

Entropy

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“…In [7], a new ant colony-based optimization method for numerous standard applications has been proposed. A cockroach swarm optimization was presented in [8] to determine paths with the shortest travel time. A whale optimization approach has applied to renewable energy impact on sustainable development [9].…”

Section: Introductionmentioning

confidence: 99%

A Novel Quantum Inspired Particle Swarm Optimization Algorithm for Electromagnetic Applications

Rehman

et al. 2020

IEEE Access

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Quantum inspired particle swarm optimization (QPSO) stimulated by perceptions from particle swarm optimization and quantum mechanics is a stochastic optimization method. Although, it has shown good performance in finding the optimal solution to many electromagnetic problems. However, sometimes it falls to local optima when dealing with hard optimization problems. Thus, to preserve a good balance between local and global searches to avoid premature convergence in quantum particle swarm optimization, this paper proposed three enhancements to the original QPSO method, the proposed method is called modified quantum particle swarm optimization (MQPSO) algorithm. Firstly, a novel selection technique is introduced that will choose the best particle among the population within the search domain to achieve a high-performance exploration. Secondly, a new mutation method is used to preserve the easiness of available QPSOs. Also, a dynamic parameter strategy is proposed for further facilitating the algorithm and tradeoff between exploration and exploitation searches. The experimental results obtained by solving standard benchmark functions and an electromagnetic design problem which is the superconducting magnetic energy storage (SMES) system available in both three parameters and eight parameters problems are reported to showcase the usefulness of the proposed approach.

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“…Many well-known meta-heuristic algorithms have been proposed to solve combinatorial problems. For example, the genetic algorithm (GA) [2], particle swarm optimization (PSO) [3], wolf search algorithm (WSA) [4] and dynamic group optimization algorithm (DGO) [5][6][7]. Despites the success in global optimization problems, meta-heuristics algorithms still suffer from several drawbacks.…”

Section: Introductionmentioning

confidence: 99%

Cross Entropy Method Based Hybridization of Dynamic Group Optimization Algorithm

Tang¹,

Fong²,

Dey

et al. 2017

Entropy

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Abstract:Recently, a new algorithm named dynamic group optimization (DGO) has been proposed, which lends itself strongly to exploration and exploitation. Although DGO has demonstrated its efficacy in comparison to other classical optimization algorithms, DGO has two computational drawbacks. The first one is related to the two mutation operators of DGO, where they may decrease the diversity of the population, limiting the search ability. The second one is the homogeneity of the updated population information which is selected only from the companions in the same group. It may result in premature convergence and deteriorate the mutation operators. In order to deal with these two problems in this paper, a new hybridized algorithm is proposed, which combines the dynamic group optimization algorithm with the cross entropy method. The cross entropy method takes advantage of sampling the problem space by generating candidate solutions using the distribution, then it updates the distribution based on the better candidate solution discovered. The cross entropy operator does not only enlarge the promising search area, but it also guarantees that the new solution is taken from all the surrounding useful information into consideration. The proposed algorithm is tested on 23 up-to-date benchmark functions; the experimental results verify that the proposed algorithm over the other contemporary population-based swarming algorithms is more effective and efficient.

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Cockroach Swarm Optimization Algorithm for Travel Planning

Cited by 21 publications

References 36 publications

A Swarm-Based Approach to Generate Challenging Mazes

A Swarm-Based Approach to Generate Challenging Mazes

A Novel Quantum Inspired Particle Swarm Optimization Algorithm for Electromagnetic Applications

Cross Entropy Method Based Hybridization of Dynamic Group Optimization Algorithm

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