Since nature is an excellent source of inspiration for optimization methods, many optimization algorithms have been proposed, are inspired by nature, and are modified to solve various optimization problems. This paper...
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Since nature is an excellent source of inspiration for optimization methods, many optimization algorithms have been proposed, are inspired by nature, and are modified to solve various optimization problems. This paper uses metaheuristics in a new field inspired by nature;more precisely, we use pollination optimization in cocoa plants. The cocoa plant was chosen as the object since its flower type differs from other kinds of flowers, for example, by using cross-pollination. This complex relationship between plants and pollinators also renders pollination a real-world problem for chocolate production. Therefore, this study first identified the underlying optimization problem as a deferred fitness problem, where the quality of a potential solution cannot be immediately determined. Then, the study investigates how metaheuristicalgorithms derived from three well-known techniques perform when applied to the flower pollination problem. The three techniques examined here are Swarm Intelligence algorithms, Individual Random search, and Multi-Agent Systems search. We then compare the behavior of these various search methods based on the results of pollination simulations. The criteria are the number of pollinated flowers for the trees and the amount and fairness of nectar pickup for the pollinator. Our results show that Multi-Agent System performs notably better than other methods. The result of this study are insights into the co-evolution of behaviors for the collaborative pollination task. We also foresee that this investigation can also help farmers increase chocolate production by developing methods to attract and promote pollinators.
Feature selection is a critical preprocessing technique used to remove irrelevant and redundant features from datasets while maintaining or improving the accuracy of machine learning models. Recent advancements in thi...
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Feature selection is a critical preprocessing technique used to remove irrelevant and redundant features from datasets while maintaining or improving the accuracy of machine learning models. Recent advancements in this area have primarily focused on wrapper-based feature selection methods, which leverage metaheuristic search algorithms (MSAs) to identify optimal feature subsets. In this paper, we propose a novel wrapper-based feature selection method utilizing the Triangulation Topology Aggregation Optimizer (TTAO), a newly developed algorithm inspired by the geometric properties of triangular topology and similarity. To adapt the TTAO for binary feature selection tasks, we introduce a conversion mechanism that transforms continuous decision variables into binary space, allowing the TTAO-which is inherently designed for real-valued problems-to function efficiently in binary domains. TTAO incorporates two distinct search strategies, generic aggregation and local aggregation, to maintain an effective balance between global exploration and local exploitation. Through extensive experimental evaluations on a wide range of benchmark datasets, TTAO demonstrates superior performance over conventional MSAs in feature selection tasks. The results highlight TTAO's capability to enhance model accuracy and computational efficiency, positioning it as a promising tool to advance feature selection and support industrial innovation in data-driven tasks.
This paper describes the selection of parameters of an Evolutionary algorithm (EA) suitable for optimising the genotype of a fractal model of phenotypically realistic structures. To achieve the proposed goal an EA is ...
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ISBN:
(纸本)9783031265037;9783031265044
This paper describes the selection of parameters of an Evolutionary algorithm (EA) suitable for optimising the genotype of a fractal model of phenotypically realistic structures. To achieve the proposed goal an EA is implemented as a metaheuristicsearch tool to find the coefficients of the transformation matrices of an Iterated Function System (IFS) which then generates regular fractal patterns. Fractal patterns occur throughout nature, a striking example being the fern patterns modelled by Barnsley. Thus the algorithm is evaluated using the IFS for the fern fractal using the EA-evolved parameters.
In this chapter, the two primarily important highly nonlinear design problems of the contemporary microwave engineering which are "Low Noise Amplifier (LNA)"s and "Reflect-array Antenna (RA)"s are ...
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ISBN:
(数字)9783319275178
ISBN:
(纸本)9783319275178;9783319275154
In this chapter, the two primarily important highly nonlinear design problems of the contemporary microwave engineering which are "Low Noise Amplifier (LNA)"s and "Reflect-array Antenna (RA)"s are solved as "Design Optimization problems." For this purpose, firstly the design problem is defined in terms of the feasible design variables (FDVs), the feasible design target space (FDTS), both of which are built up by integrating the artificial intelligence black-box models based upon the measurements or full-wave simulations and a suitable metaheuristic search algorithm. In the second stage, feasible design target (FDT) or objective function of the optimization procedure is determined as a sub-space of the FDTS. Thirdly, the cost function evaluating the objective is minimized employing a suitable metaheuristic search algorithm with respect to the FDVs. Finally the completed designs are verified by the professional Microwave Circuitor3-D EM simulators.
作者：
Shahi, ShashiPulkki, ReinoLakehead Univ
Fac Nat Resources Management Thunder Bay ON P7B 7C4 Canada Lakehead Univ
Fac Nat Resources Management Forest Operat & Transport Logist Thunder Bay ON P7B 7C4 Canada
This paper develops a simulation-based optimization supply chain model for supplying sawlogs to a sawmill from a forest management unit. The simulation model integrates the two-way flow of information and materials un...
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This paper develops a simulation-based optimization supply chain model for supplying sawlogs to a sawmill from a forest management unit. The simulation model integrates the two-way flow of information and materials under the stochastic demand of the sawmill production unit. The dynamic optimization model finds the optimum inventory policy (s, S) that minimizes the total inventory cost for the three supply chain agents - sawmill storage, merchandizing yard, and forest management unit. The model is used to analyze a real sawmill case study in northwestern Ontario, Canada. It was found that the merchandizing yard absorbs shocks of uncertain demand from the sawmill production unit and reduces idle time, but it increases the total cost of the supply chain by $11 802 (about 42%). The optimized model predicts that only 3.5 days of inventory is required at the sawmill storage. The simulation-based optimization supplier model will help in decision-making at the tactical and operational level in the forest products industry supply chain through a two-way flow of information and materials.
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