Five-elements cycle optimization algorithm for the travelling salesman problem

Liu, Mandan

doi:10.1109/icar.2017.8023672

Cited by 4 publications

(10 citation statements)

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“…In this section, we first describe the related work of FECO, which was proposed in Reference [19] for solving single-objective problems. Then, we elaborate the motivation of extending FECO to its multi-objective version, Multi-Objective Five-Elements Cycle Optimization algorithm (MOFECO), by which multi-objective problems can be solved.…”

Section: Related Work and The Motivation Of This Papermentioning

confidence: 99%

“…The literature [19] first puts forward the FECM, which draws on the dynamic system equilibrium relationship described by the five elements of ancient Chinese philosophy. The five elements are metal, wood, water, fire and earth.…”

Section: Related Work Of Five-elements Cycle Optimization (Feco)mentioning

confidence: 99%

“…As mentioned above, each element is influenced by the other four in a certain way, and this way is the force relationship between the five elements. The FECM proposed in Reference [19] is described as follows. Suppose a dynamic system consists of the five elements shown in Figure 2.…”

Section: Related Work Of Five-elements Cycle Optimization (Feco)mentioning

confidence: 99%

“…In the literature [19], a new Five-Elements Cycle Model (FECM) derived from ancient Chinese philosophy which is based on the Yin-Yang theory and Five Elements (metal, wood, water, fire, earth) is proposed. FECM was established based on the relationship of generation and restriction among the five elements.…”

Section: Introductionmentioning

confidence: 99%

“…In FECM, the fitness value of each element is measured by calculating the interaction between elements, and then the element is updated according to its strengths and weaknesses. On the basis of FECM, Five-Elements Cycle Optimization algorithm (FECO) effectively solves both continuous function optimization problems and Traveling Salesman Problems (TSP) [19,20].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Multi-Objective Five-Elements Cycle Optimization Algorithm

2019

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Inspired by the mechanism of generation and restriction among five elements in Chinese traditional culture, we present a novel Multi-Objective Five-Elements Cycle Optimization algorithm (MOFECO). During the optimization process of MOFECO, we use individuals to represent the elements. At each iteration, we first divide the population into several cycles, each of which contains several individuals. Secondly, for every individual in each cycle, we judge whether to update it according to the force exerted on it by other individuals in the cycle. In the case of an update, a local or global update is selected by a dynamically adjustable probability P s ; otherwise, the individual is retained. Next, we perform combined mutation operations on the updated individuals, so that a new population contains both the reserved and updated individuals for the selection operation. Finally, the fast non-dominated sorting method is adopted on the current population to obtain an optimal Pareto solution set. The parameters’ comparison of MOFECO is given by an experiment and also the performance of MOFECO is compared with three classic evolutionary algorithms Non-dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization algorithm (MOPSO), Pareto Envelope-based Selection Algorithm II (PESA-II) and two latest algorithms Knee point-driven Evolutionary Algorithm (KnEA) and Non-dominated Sorting and Local Search (NSLS) on solving test function sets Zitzler et al’s Test suite (ZDT), Deb et al’s Test suite (DTLZ), Walking Fish Group (WFG) and Many objective Function (MaF). The experimental results indicate that the proposed MOFECO can approach the true Pareto-optimal front with both better diversity and convergence compared to the five other algorithms.

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Section: Related Work and The Motivation Of This Papermentioning

confidence: 99%

Section: Related Work Of Five-elements Cycle Optimization (Feco)mentioning

confidence: 99%

Section: Related Work Of Five-elements Cycle Optimization (Feco)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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