Search results

This study examines fisheries affected by the Great East Japan Earthquake and Tsunami in 2011 to explore how the collectivism appears during the recovery process.

The author questions the context of collaboration after the disaster among independent small-scale fishers in Miyagi by conducting semistructured interviews with more than 50 local fishers with anthropological observations of boat fishing operations and using local documents and statistics.

The corresponding collaboration among the fishers after the disaster is not a mere “disaster utopia,” but is embedded in the socioecological context of fishing. Fishers have developed individual and group fishing. They have institutionalized competitive distribution for sedentary fish with low resource fluctuation, while outcome-equal distribution is adopted for migratory fish with high resource fluctuation. This forms a fishing continuum that connects competitive individualism with collectivism in the community, which has contributed to resilience for disaster recovery.

The balance between individualism and collectivism is decisively coordinated in socioecological contexts. The multifaceted resource strategy for maritime biodiversity that features family-based occupational differentiation in a community is crucial for disaster recovery of small-scale fishers.

The purpose of this paper is to execute the efficiency analysis of the selected metaheuristic algorithms (MAs) based on the investigation of analytical functions and investigation optimization processes for permanent magnet motor.

A comparative performance analysis was conducted for selected MAs. Optimization calculations were performed for as follows: genetic algorithm (GA), particle swarm optimization algorithm (PSO), bat algorithm, cuckoo search algorithm (CS) and only best individual algorithm (OBI). All of the optimization algorithms were developed as computer scripts. Next, all optimization procedures were applied to search the optimal of the line-start permanent magnet synchronous by the use of the multi-objective objective function.

The research results show, that the best statistical efficiency (mean objective function and standard deviation [SD]) is obtained for PSO and CS algorithms. While the best results for several runs are obtained for PSO and GA. The type of the optimization algorithm should be selected taking into account the duration of the single optimization process. In the case of time-consuming processes, algorithms with low SD should be used.

The new proposed simple nondeterministic algorithm can be also applied for simple optimization calculations. On the basis of the presented simulation results, it is possible to determine the quality of the compared MAs.