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A parameter-free discrete particle swarm algorithm and its application to multi-objective pavement maintenance schemes
Regular maintenance is paramount for a healthy road network, the arteries of any economy. As the resources for
maintenance are limited, optimization is necessary. A number of conflicting objectives exist with many influencing
variables. Although many methods have been proposed, the related research is very active, due to difficulties
in adoption to the actual practice owing to reasons such high-dimensional problems even for small road
networks. Literature survey tells that particle swarms have not been exploited much, mainly due to unavailability
of many techniques in this domain for multi-objective discrete problems like this. In this work, a novel particle
swarm algorithm is proposed for a general, discrete, multi-objective problem. In contrast to the standard particle
swarm, the bare-bones technique has a clear advantage in that it is a parameter-free technique, hence the end
users need not be optimization experts. However, the existing barebones algorithm is available only for continuous
domains, sans any particle velocity terms. For discrete domains, the proposed method introduces a
parameter-free velocity term to the standard bare-bones algorithm. Based on the peak velocities observed by the
different dimensions of a particle, its new position is calculated. A number of benchmark test functions are also
solved. The results show that the proposed algorithm is highly competitive and able to obtain much better spread
of solutions compared to three other existing PSO and genetic algorithms. The method is benchmarked against a
number of other algorithms on an actual pavement maintenance problem. When compared against another
particle swarm algorithm, it not only shows better performance, but also significant reduction in run-time
compared to other POS algorithm. Hence, for large road network maintenance, the proposed method shows a lot of promise in terms of analysis time, while improving on the quality of solutions
From the social learning theory to a social learning algorithm for global optimization
Traditionally, the Evolutionary Computation (EC) paradigm is inspired by Darwinian evolution or the swarm intelligence of animals. Bandura's Social Learning Theory pointed out that the social learning behavior of humans indicates a high level of intelligence in nature. We found that such intelligence of human society can be implemented by numerical computing and be utilized in computational algorithms for solving optimization problems. In this paper, we design a novel and generic optimization approach that mimics the social learning process of humans. Emulating the observational learning and reinforcement behaviors, a virtual society deployed in the algorithm seeks the strongest behavioral patterns with the best outcome. This corresponds to searching for the best solution in solving optimization problems. Experimental studies in this paper showed the appealing search behavior of this human intelligence-inspired approach, which can reach the global optimum even in ill conditions. The effectiveness and high efficiency of the proposed algorithm has further been verified by comparing to some representative EC algorithms and variants on a set of benchmarks
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