LSHADE-SPA memetic framework for solving large-scale optimization problems

Hadi, Anas A.; Mohamed, Ali Wagdy; Jambi, Kamal

doi:10.1007/s40747-018-0086-8

Cited by 73 publications

(45 citation statements)

References 38 publications

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“…In this section, the performance of the proposed F3C algorithm is compared with those of the following state-of-theart ones in the literature, namely, CCFR2 [45], two-phase learning-based swarm optimizer (TPLSO) [58], FCRACC [44], affinity propagation assisted and evolution consistency based decomposition (APEC) [59], CCFR [42], CC with optimizer selection (CCOS) [38], CBCC1 [12], CBCC2 [12], LSHADE semi-parameter adaptation memetic framework (MLSHADE-SPA) [60], competitive swarm optimizer (CSO) [61], enhanced adaptive differential evolution (EADE) [62], multiple offspring sampling (MOS) [63] and memetic algorithm based on local search chains (MA-SW-Chain) [25].…”

Section: Comparisons Of Proposed and State-of-the-art Algorithmsmentioning

confidence: 99%

Contribution Based Co-Evolutionary Algorithm for Large-Scale Optimization Problems

et al. 2020

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The solution of large-scale optimization problems is the key to many decision-making processes in practice. However, it is a challenging research topic when considered both the quality of solutions and the required computational time. One of the popular approaches for these problems is to divide the problems into a number of smaller sub-problems, that are then solved separately with an exchange of some information using the cooperative co-evolution (CC) concept. However, the characteristics of subcomponents could be different, and their contributions to the overall performance can also be different while solving the problem. In the CC approach, it usually applies one optimizer and allocates equal computational budget to all sub-components. In this paper, a new algorithm is proposed with the use of multiple optimizers, along with a need-based allocation of computational budget for the sub-components. In the proposed algorithm, a group of optimizers cooperate in an effective way to evolve the sub-components, depending on heuristic fuzzy rules. The performance of our proposed algorithm was evaluated by solving a number of large-scale global optimization benchmark functions. The empirical results show that the proposed algorithm outperforms equal allocation CC, a single selection characteristic, a single candidate optimizer and state-of-the-art algorithms. INDEX TERMS Cooperative co-evolution, large-scale optimization, fuzzy logic.

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Section: Comparisons Of Proposed and State-of-the-art Algorithmsmentioning

confidence: 99%

Contribution Based Co-Evolutionary Algorithm for Large-Scale Optimization Problems

et al. 2020

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“…With respect to this, the direct search Powell's method has been combined with DE (DESAP) [23], where the near-global solution obtained by the DE algorithm is improved using Powell's method. To enhance the performance, the hybridization between DE and a local search algorithm (MLSHADE-SPA), with linear population size reduction and semiparameter adaptation, has been proposed [38]. Furthermore, the DE algorithm has been hybridized with Hook-Jeeves in distributed memetic DE algorithm (DMDE), to efficiently find the global optimum and achieve a better trade-off between the exploration and the exploitation [24].…”

Section: Background and Related Workmentioning

confidence: 99%

Passive Target Localization Problem Based on Improved Hybrid Adaptive Differential Evolution and Nelder-Mead Algorithm

2020

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This paper considers a passive target localization problem in Wireless Sensor Networks (WSNs) using the noisy time of arrival (TOA) measurements, obtained from multiple receivers and a single transmitter. The objective function is formulated as a maximum likelihood (ML) estimation problem under the Gaussian noise assumption. Consequently, the objective function of the ML estimator is a highly nonlinear and nonconvex function, where conventional optimization methods are not suitable for this type of problem. Hence, an improved algorithm based on the hybridization of an adaptive differential evolution (ADE) and Nelder-Mead (NM) algorithms, named HADENM, is proposed to find the estimated position of a passive target. In this paper, the control parameters of the ADE algorithm are adaptively updated during the evolution process. In addition, an adaptive adjustment parameter is designed to provide a balance between the global exploration and the local exploitation abilities. Furthermore, the exploitation is strengthened using the NM method by improving the accuracy of the best solution obtained from the ADE algorithm. Statistical analysis has been conducted, to evaluate the benefits of the proposed modifications on the optimization performance of the HADENM algorithm. The comparison results between HADENM algorithm and its versions indicate that the modifications proposed in this paper can improve the overall optimization performance. Furthermore, the simulation shows that the proposed HADENM algorithm can attain the Cramer-Rao lower bound (CRLB) and outperforms the constrained weighted least squares (CWLS) and differential evolution (DE) algorithms. The obtained results demonstrate the high accuracy and robustness of the proposed algorithm for solving the passive target localization problem for a wide range of measurement noise levels.

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“…They are used to build denotative semantics. However, it should be noted that the juxtaposition of each factor or connection of the AIs' models with some relevant subset of big data will require the use of special methods for solving problems on cognitive models, namely, methods for solving large-scale global optimization problems [15]. But due to the very high requirements for the promptness of solving problems in emergencies, most likely, such an approach for such situations is unacceptable.…”

Section: Situational Awarenessmentioning

confidence: 99%

Accelerating Decision-Making in Transport Emergency with Artificial Intelligence

Raikov¹

2020

Adv. sci. technol. eng. syst. j.

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The paper addresses speeding up meetings in a networked environment during rescue works in a transport emergency. Several groups of representatives of various services and observers participate in those meetings. The number of wrong decisions tends to increase because remote participants cannot understand each other quickly. First, the meetings must be efficiently held to avoid making wrong decisions, including medical diagnoses for injuries. The ultimate goals are to improve injured' health and life. Artificial intelligence (AI), big data analysis, and deep learning methods suggested in this paper for decisionmaking support have a cognitive character, i.e., try to take into account the thoughts and emotions of participants. The author's convergent approach ensures the purposefulness and sustainability of decision-making. This approach transforms divergent decision-making processes into convergent. The approach is based on the inverse problem-solving method in topological space, genetic algorithms, control thermodynamic theory, and using the ideas of creating AI models' cognitive semantics with quantum mechanics methods. This approach gives meetings' members the list of decision-making rules with accelerating consensus achievement. The examples of the rules are: the goals have to be arranged as a 3-level tree and ordered by importance; semantic interpretations of computer models' factors and their connections must be separated; rescue resources must be represented in a finite number of separated components, and so on. The approach also exploits traditional technical tools of augmented reality, virtual collaboration, and situational awareness. It has been repeatedly used to build socioeconomic and manufacturing sectoral strategies and is currently being adapted for emergencies.

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LSHADE-SPA memetic framework for solving large-scale optimization problems

Cited by 73 publications

References 38 publications

Contribution Based Co-Evolutionary Algorithm for Large-Scale Optimization Problems

Contribution Based Co-Evolutionary Algorithm for Large-Scale Optimization Problems

Passive Target Localization Problem Based on Improved Hybrid Adaptive Differential Evolution and Nelder-Mead Algorithm

Accelerating Decision-Making in Transport Emergency with Artificial Intelligence

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