https://fada.birzeit.edu/bitstream/20.500.11889/5314/1/Hybrid%20Whale%20Optimization%20Algorithm%20with%20Simulated%20Annealing%20for%20FS.pdf

Hybrid Whale Optimization Algorithm with Simulated Annealing for Feature Selection

Symbiotic Organisms Search (SOS) algorithm is an effective new metaheuristic search algorithm, which has recently recorded wider application in solving complex optimization problems. SOS mimics the symbiotic relationship strategies adopted by organisms in the ecosystem for survival. This paper, presents a study on the application of SOS with Simulated Annealing (SA) to solve the well-known traveling salesman problems (TSPs). The TSP is known to be NP-hard, which consist of a set of (n1)!/2 feasible solutions. The intent of the proposed hybrid method is to evaluate the convergence behaviour and scalability of the symbiotic organism's search with simulated annealing to solve both small and large-scale travelling salesman problems. The implementation of the SA based SOS (SOS-SA) algorithm was done in the MATLAB environment. To inspect the performance of the proposed hybrid optimization method, experiments on the solution convergence, average execution time, and percentage deviations of both the best and average solutions to the best known solution were conducted. Similarly, in order to obtain unbiased and comprehensive comparisons, descriptive statistics such as mean, standard deviation, minimum, maximum and range were used to describe each of the algorithms, in the analysis section. The Friedman's Test (with post hoc tests) was further used to compare the significant difference in performance between SOS-SA and the other selected state-of-the-art algorithms. The performances of SOS-SA and SOS are evaluated on different sets of TSP benchmarks obtained from TSPLIB (a library containing samples of TSP instances). The empirical analysis results show that the quality of the final results as well as the convergence rate of the new algorithm in some cases produced even more superior solutions than the best known TSP benchmarked results.

Simulated annealing based symbiotic organisms search optimization algorithm for traveling salesman problem

In this paper, a novel hybrid optimization algorithm is introduced by hybridizing a Harris hawks optimization algorithm(HHO) and simulated annealing for the purpose of accelerating its glo...

A novel hybrid Harris hawks-simulated annealing algorithm and RBF-based metamodel for design optimization of highway guardrails

Precast supply chain management in off-site construction: A critical literature review

In recent years, hybrid genetic algorithms (GAs) have received significant interest and are widely being used to solve real-world problems. The hybridization of heuristic methods aims at incorporating benefits of stand-alone heuristics in order to achieve better results for the optimization problem. In this paper, we propose a hybridization of GAs and Multiagent Reinforcement Learning (MARL) heuristic for solving Traveling Salesman Problem (TSP). The hybridization process is implemented by producing the initial population of GA, using MARL heuristic. In this way, GA with a novel crossover operator, which we have called Smart Multi-point crossover, acts as tour improvement heuristic and MARL acts as construction heuristic. Numerical results based on several TSP datasets taken from the TSPLIB demonstrate that proposed method found optimum solution of many TSP datasets and near optimum of the others and enable to compete with nine state-of-the-art algorithms, in terms of solution quality and CPU time.

A hybrid algorithm using a genetic algorithm and multiagent reinforcement learning heuristic to solve the traveling salesman problem

AHSATS-d-CM: Adaptive Hybrid Simulated Annealing Tabu Search Algorithm with Dynamic Neighborhood Based on Circle-directed Mutation.AHSATS-2-opt: Adaptive Hybrid Simulated Annealing Tabu Search Algorithm with 2-opt.Conclusions: This paper develops an adaptive hybrid meta-heuristic algorithm that combines simulated annealing and tabu search algorithms with a dynamic neighborhood structure to solve the Traveling Salesman Problem (TSP). The experimental results demonstrated that the proposed algorithm can obtain satisfactory solutions within a reasonable time. Moreover, the proposed algorithm can overcome the individual disadvantages of simulated annealing and tabu search. The new hybrid algorithm provides a fast decrease rate and a clear convergence process, and is not dependent on initial solutions. Furthermore, compared with the classical 2-opt neighborhood, the dynamic neighborhood can significantly reduce computational time by decreasing the number of calculations and simultaneously improve solution quality. Finally, the adaptive parameters are appropriate for solving almost all benchmark test examples.Display Omitted A hybrid simulated annealingtabusearch algorithm is applied to solve the traveling salesman problem.A dynamic neighborhood based on circle-directed mutation is developed for the hybrid algorithm.Adaptive parameters are proposed for the hybrid algorithm.Experiments are conducted to verify the accuracy and efficiency of the hybrid algorithm proposed.Experimental results are compared with other algorithms derived from literature. This paper applies a hybrid simulated annealing tabu search algorithm to solve the Traveling Salesman Problem (TSP). Fully considering the characteristics of the hybrid algorithm, we develop a dynamic neighborhood structure for the hybrid algorithm to improve search efficiency by reducing the randomness of the conventional 2-opt neighborhood. A circle-directed mutation is developed to achieve this dynamic neighborhood structure. Furthermore, we propose adaptive parameters that can be automatically adjusted by the algorithm based on context specific examples. This negates the need to frequently readjust algorithm parameters. We employ benchmarks obtained from TSPLIB (a library of sample instances for the TSP) to test our algorithm, and find that the proposed algorithm can obtain satisfactory solutions within a reasonable amount of time. The experimental results demonstrate that the proposed hybrid algorithm can overcome the disadvantages of traditional simulated annealing and tabu search methods. The results also show that the dynamic neighborhood structure is more efficient and accurate than the classical 2-opt. Also, adaptive parameters are appropriate for almost all of the numerical examples tested in this paper. Finally, the experimental results are compared with those of other algorithms, to demonstrate the improved accuracy and efficiency of the proposed algorithm.

Developing a dynamic neighborhood structure for an adaptive hybrid simulated annealing tabu search algorithm to solve the symmetrical traveling salesman problem

Ridesharing is an effective transportation mode to provide first-mile accessibility to public transit and a low-cost, environment-friendly, and sustainable mobility service. This paper designs a mechanism for the first-mile ridesharing service. The mechanism accounts for passengers’ personalized requirements on different inconvenience attributes (e.g. the number of co-riders, extra in-vehicle travel time, and extra waiting time at the transit hub) of the service in determining the optimal vehicle-passenger matching and vehicle routing plan and customized pricing scheme. The proposed mechanism is proved to be individual rational, incentive compatible, and price non-negative. The three properties respectively indicate that passengers are willing to participate in the service, that honestly reporting personalized requirements is the optimal strategy, and that the service provider is guaranteed to receive revenue from the participants. A case study is proposed to interpret the mechanism and to demonstrate the generality of the personalized-requirement-based mechanism that can be adapted into different scenarios.

Mechanism design for first-mile ridesharing based on personalized requirements part I: Theoretical analysis in generalized scenarios

Ridesharing emerges as a viable way to bridge the first-mile accessibility gap to public transit. There is particularly a high demand for the first-mile ridesharing service in transit-intensive metropolitan areas. This paper studies the mechanism design problem in order to promote passengers’ participation in the on-demand first-mile ridesharing accounting for passengers’ mobility preferences, including passengers’ requirements on arrival deadlines, maximum willing-to-pay prices, and detour tolerances. A rolling horizon planning approach is used to process spontaneous on-demand passenger requests. We propose a novel mechanism, namely “Mobility-Preference-Based Mechanism with Baseline Price Control” (MPMBPC), which adapts the traditional Vickrey–Clarke–Groves (VCG) mechanism and incorporates a baseline price control component. MPMBPC is proved to satisfy several important mechanism design properties, including “individual rationality”, “incentive compatibility”, “price controllability”, and “detour discounting reasonability”. In comparison with the traditional general-purpose VCG mechanism, MPMBPC can avoid unreasonably low prices and prevent carriers’ deficits. A computationally efficient heuristic algorithm called Solution Pooling Approach (SPA) is developed to solve large-scale ridesharing mechanism design problems. Numerical examples are developed to demonstrate that SPA can solve large-scale ridesharing mechanism design problems in a computationally efficient way, with satisfactory solution qualities.

Mechanism design for on-demand first-mile ridesharing

A wireless monitoring network is an effective way to monitor and transmit information about railway infrastructure conditions. Its lifetime is significantly affected by the energy usage among all sensors. This paper proposes a novel cluster-based valid lifetime maximization protocol (CVLMP) to extend the lifetime of the network. In the CVLMP, the cluster heads (CHs) are selected and rotated with the selection probability and energy information. Then, the clusters are determined around the CHs based on the multi-objective optimization model, which minimizes the total energy consumption and balances the consumption among all CHs. Finally, the multi-objective model is solved by an improved nondominated sorting genetic algorithm II. The simulation results show that, compared with two other strategies in the prior literature, our proposed CVLMP can effectively extend the valid lifetime of the network as well as increase the inspected data packets received at the sink node.

An Optimal Communications Protocol for Maximizing Lifetime of Railway Infrastructure Wireless Monitoring Network

Ridesharing provides travelers with a low-cost and convenient first-mile mobility service. Our Part I paper designed a mechanism to incentivize more travelers to participate in the first-mile ridesharing service accounting for passengers’ personalized requirements on inconvenience attributes of ridesharing. In order to address the computational challenge of obtaining the mechanism for large-scale transportation networks, this paper develops a novel heuristic algorithm, called the Solution Pooling Approach (SPA) for efficiently solving large-scale mechanism design problems in the first-mile ridesharing context. This paper also extends the SPA to solve generalized mechanism design problems, analyzes specific circumstances under which the SPA can sustain the game-theoretic properties, including “individual rationality” and “incentive compatibility”, and identifies its limitations. For the particular application in first-mile ridesharing, the SPA maintains the properties of “individual rationality” and “incentive compatibility”. Numerical experimental results show that the SPA can address the complex first-mile ridesharing service mechanism design problem in a computationally viable and efficient manner.

Zheyong Bian

Papers

Developing a dynamic neighborhood structure for an adaptive hybrid simulated annealing tabu search algorithm to solve the symmetrical traveling salesman problem

Mechanism design for first-mile ridesharing based on personalized requirements part I: Theoretical analysis in generalized scenarios

Mechanism design for on-demand first-mile ridesharing

An Optimal Communications Protocol for Maximizing Lifetime of Railway Infrastructure Wireless Monitoring Network

Mechanism design for first-mile ridesharing based on personalized requirements part II: Solution algorithm for large-scale problems