An improved immune plasma algorithm with a regional pandemic restriction

Abstract

The coronavirus and its global effect have increased the interests of researchers from different disciplines to the medical methods such as immune or convalescent plasma treatment. Immune Plasma algorithm (IPA) that is the first meta-heuristic referencing the steps of the immune plasma treatment as the name implies has been proposed recently and its potential has been investigated. In this study, a pandemic management strategy based on limiting the free movements between regions was modeled and integrated into the workflow of the IPA and a new variant called regional IPA (rIPA) was introduced. For analyzing the contribution of the proposed method, twelve numerical benchmark problems were solved. Also, the performance of the rIPA was investigated by solving a new big data optimization problem that requires minimization of the measurement noise of electroencephalography signals. The results obtained by the rIPA were compared with the fourteen well-known and state-of-art meta-heuristics. Comparative studies showed that managing the relationship between the individuals of the population as in the proposed regional model significantly contributes to the capabilities and rIPA outperforms other meta-heuristics for most of the test cases.

Path Planning of an Unmanned Combat Aerial Vehicle with an Extended-Treatment-Approach-Based Immune Plasma Algorithm

“…The performance validation of ExtIPA was concluded after a comparison with other meta-heuristic algorithms, including IPA, ABC, BA, ACO, BBO, DE, ES, FA, GA, PBIL, and PSO and some of their variants, such as BAM, MFA, SGA, and PGSO, in the fifth battlefield scenario. The path planners used for the test cases of the fifth battlefield were run 100 times by setting the population size to 30 and the maximum evaluation number to 6000 [21,57]. The mean best, best, and worst objective function values and related standard deviations of them calculated after 100 independent runs are given in Table 8.…”

“…If the main operations executed by the IPA are investigated, it can be easily seen that the IPA differs from the most representative meta-heuristics including ACO, ABC, PSO, BA, FA, FWA, DE, and GA [53]. While the exploitation-dominant operations of the IPA are designed in a semi-adaptive manner and adjust the convergence dynamically without changing the initial values of the control parameters, a quasi-deterministic approach is responsible for managing the important part of exploration, and its promising performance has been validated recently for engineering problems such as channel assignment [54], time series prediction [55], wireless sensor deployment [56], signal noise minimization [57], neural network training [58], and also UAV or UCAV path planning [59]. In this study, the plasma transfer scheme of the IP algorithm was changed with a newly introduced approach called extended treatment, and an extended IP algorithm, or ExtIPA, was designed to solve the path-planning problem.…”