Social Evolution: An Evolutionary Algorithm Inspired by Human Interactions

Pavithr, R. S.; Gursaran,

doi:10.1007/978-81-322-1602-5_153

Cited by 3 publications

(4 citation statements)

References 7 publications

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“…Here the QSE was compared against the GQA [22], QEA [23], QEA with probabilistic termination conditions [24], two phases TPQEA [24], advanced quantum genetic algorithm (ANQGA) [77], random search and greedy selection based GQA (RSGS_GQA) [53], quantum-inspired evolutionary algorithm based on p-system (QEPS) [76], adaptive quantum-inspired differential evolution algorithm (AQDE) [28], quantum-inspired cuckoo algorithm (QICSA) [38], quantum-inspired harmony search algorithm (QIHSA) [39], quantum-inspired DE and PSO algorithm (QDEPSO) [79], quantum-inspired Swarm Evolution (QSwEv) [68], discrete binary differential algorithm [28], binary artificial bee colony optimization and binary PSO (BABCNBPSO) [26], different binary PSO variants (DBPSO) [4]. In [54], the QSE was tested using the 0-1 knapsack problem, and it was reported that the QSE has better performance than the GQA, QEA, QEA with different termination conditions, TPQEA, ANQEA, RSGS_GQA, QEPS, AQDE, QSwEv, DBPSO, and BABCNBPSO. There was not reported a comparative of convergence time of each algorithm.…”

Section: Analysis Of the Methodsmentioning

confidence: 99%

“…Human interactions, knowledge, and opinions inspired social evolution algorithms [54]. The first algorithm that used these concepts is the human evolutionary model (HEM) [46].…”

Section: Social Evolution Algorithmmentioning

confidence: 99%

“…HEM through MFL maintains a minimum population size to exploit search space regions very fast; when diversity is lost, the population size increases to recover the exploration ability until a new promising region is found. In [54], a mechanism to incorporate human bias in the selection of individuals through a second opinion when interactions between individuals are indecisive was proposed. Algorithm 15 shows the pseudocode of the quantum-inspired social evolution algorithm (QSE) [52].…”

Section: Social Evolution Algorithmmentioning

confidence: 99%

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A Review of Quantum-Inspired Metaheuristics: Going From Classical Computers to Real Quantum Computers

Montiel

2020

IEEE Access

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This paper presents a review of quantum-inspired population-based metaheuristics. Quantuminspired algorithms were born when there were no quantum computers; they demonstrated to have interesting characteristics providing good results in classical computers. At present, when the first quantum computers are available, scientists are working to confirm the quantum supremacy in different fields. After almost 20 years that the first metaheuristic inspired in quantum phenomena was published, a large number of works have been proposed. This paper aims to look back to see which quantum-inspired metaheuristics could be translated to be used in the existing quantum computers based on the circuit model programming paradigm. Reviewed metaheuristics were classified according to their main source of inspiration; just some representative works of each classification were selected because of the vast number of existing works on each one. The analysis was done for the circuit model and metrics as width, size, and length were used to determine their viability of being implemented in a real quantum computer. Moreover, comparative results using metrics such as performance and running time for quantum-inspired metaheuristic were included. INDEX TERMS Quantum metaheuristic, quantum-inspired, circuit model, metaheuristic for quantum computer, binary coded quantum-inspired metaheuristic, real coded quantum-inspired metaheuristic.

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Section: Analysis Of the Methodsmentioning

confidence: 99%

“…Human interactions, knowledge, and opinions inspired social evolution algorithms [54]. The first algorithm that used these concepts is the human evolutionary model (HEM) [46].…”

Section: Social Evolution Algorithmmentioning

confidence: 99%

Section: Social Evolution Algorithmmentioning

confidence: 99%

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A Review of Quantum-Inspired Metaheuristics: Going From Classical Computers to Real Quantum Computers

Montiel

2020

IEEE Access

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“…A new imperialist competitive algorithm is proposed in [25] that combines evolutionary algorithm and sociopolitical process; this approach tries to get people who live in diferent type of communities involved in development of the whole space. Social evolution algorithm is inspired by the human interactions which are selective and can explore randomly based on the individual characteristics [26]. Te individuals interact with others in a variety of ways and adopt the tactics to emerge or evolve.…”

Section: Introductionmentioning

confidence: 99%

Evolving LSTM Networks for Time‐Series Classification in EdgeIoT

Cui¹,

Jiang

et al. 2023

Mathematical Problems in Engineering

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We proposed a novel approach to evolve LSTM networks utilizing intelligent optimization algorithms and address time-series classification problems in EdgeIoT. Meanwhile, a new optimizer called cultural society and civilization (CSC) algorithm is proposed to reduce the probability of stagnated in the local optima and increase the convergence speed. The suggested method could relieve the problem that the traditional data mining and pattern extraction methods cannot guarantee high accuracy and are hard to deploy on terminal devices. The proposed CSC algorithm and CSC-optimized LSTM model is examined on benchmark problems and demonstrates remarkable superiority over traditional methods and can be applied to support EdgeIoT for learning and processing.

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Quantum Inspired Social Evolution (QSE) algorithm for 0-1 knapsack problem

Pavithr

Gursaran

2016

Swarm and Evolutionary Computation

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Social Evolution: An Evolutionary Algorithm Inspired by Human Interactions

Cited by 3 publications

References 7 publications

A Review of Quantum-Inspired Metaheuristics: Going From Classical Computers to Real Quantum Computers

A Review of Quantum-Inspired Metaheuristics: Going From Classical Computers to Real Quantum Computers

Evolving LSTM Networks for Time‐Series Classification in EdgeIoT

Quantum Inspired Social Evolution (QSE) algorithm for 0-1 knapsack problem

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