Abstract:This paper proposes total optimization of energy networks in a smart city by multi-population global-best modified brain storm optimization (MP-GMBSO). Efficient utilization of energy is necessary for reduction of CO2 emission, and smart city demonstration projects have been conducted around the world in order to reduce total energies and the amount of CO2 emission. The problem can be formulated as a mixed integer nonlinear programming (MINLP) problem and various evolutionary computation techniques such as par… Show more
“…The whale optimization algorithm (WAO) 12 simulates the hunting of prey, prey envelopment, and the bubble net hunting behaviour of humpback whales. There are also some nature-inspired and animal-inspired algorithms that are extensively used by researchers in various fields, such as path design 13 – 15 , control autoregressive models 16 – 19 and urban development 20 , 21 .…”
High-dimensional optimization has numerous potential applications in both academia and industry. It is a major challenge for optimization algorithms to generate very accurate solutions in high-dimensional search spaces. However, traditional search tools are prone to dimensional catastrophes and local optima, thus failing to provide high-precision results. To solve these problems, a novel hermit crab optimization algorithm (the HCOA) is introduced in this paper. Inspired by the group behaviour of hermit crabs, the HCOA combines the optimal search and historical path search to balance the depth and breadth searches. In the experimental section of the paper, the HCOA competes with 5 well-known metaheuristic algorithms in the CEC2017 benchmark functions, which contain 29 functions, with 23 of these ranking first. The state of work BPSO-CM is also chosen to compare with the HCOA, and the competition shows that the HCOA has a better performance in the 100-dimensional test of the CEC2017 benchmark functions. All the experimental results demonstrate that the HCOA presents highly accurate and robust results for high-dimensional optimization problems.
“…The whale optimization algorithm (WAO) 12 simulates the hunting of prey, prey envelopment, and the bubble net hunting behaviour of humpback whales. There are also some nature-inspired and animal-inspired algorithms that are extensively used by researchers in various fields, such as path design 13 – 15 , control autoregressive models 16 – 19 and urban development 20 , 21 .…”
High-dimensional optimization has numerous potential applications in both academia and industry. It is a major challenge for optimization algorithms to generate very accurate solutions in high-dimensional search spaces. However, traditional search tools are prone to dimensional catastrophes and local optima, thus failing to provide high-precision results. To solve these problems, a novel hermit crab optimization algorithm (the HCOA) is introduced in this paper. Inspired by the group behaviour of hermit crabs, the HCOA combines the optimal search and historical path search to balance the depth and breadth searches. In the experimental section of the paper, the HCOA competes with 5 well-known metaheuristic algorithms in the CEC2017 benchmark functions, which contain 29 functions, with 23 of these ranking first. The state of work BPSO-CM is also chosen to compare with the HCOA, and the competition shows that the HCOA has a better performance in the 100-dimensional test of the CEC2017 benchmark functions. All the experimental results demonstrate that the HCOA presents highly accurate and robust results for high-dimensional optimization problems.
“…In order to travel more efficiently using different modes of transportation, Kwiecien [17] applies a cockroach swarm optimization algorithm to solve the path problem with the shortest travel time. Sato [18] introduces a multi-swarm DE and PSO in order to optimize the energy network of a smart city. Jia [19] considers the problem of electric vehicle customers' power constraints and proposes a novel bilevel ant colony optimization algorithm for generating routes that satisfy customers' requirements.…”
The minimax problem in continuous high-dimensional spaces has been a challenge in optimization. Traditional optimization algorithms cannot balance well between depth search and breadth search in high dimensional search spaces. A new hermit crab optimization algorithm (HCOA) is introduced in this paper to address these problems. Inspired by the population behavior of hermit crabs, the hermit crab optimization algorithm introduces the optimal crab memory and the backtracking search around the memory. Compared with other metaheuristic algorithms, the hermit crab optimization algorithm does not require advanced training or parameter correction and thus can be more quickly employed for different optimization problems. To explore the capabilities of HCOA, the simulation experiment selected CEC2017 as the test function and five well-known optimization algorithms as the control group. Among the 29 benchmark features in the CEC2017, HCAO ranks first in the number of features with 23, and second, third and fifth with two each. Experimental results demonstrate that HCOA present highly accurate and robust results for high-dimensional optimization problems.
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