Determining The Paths of Urban Expansion for The City of Kirkuk Using The Hierarchical Analysis – Multi - Criteria Decision - Making Method

The research aims to choose the paths of urban expansion byadopting a correct scientific approach that enhances themethodology of spatial analysis of the most appropriate sitesby studying indicators that affect the city, and since thehierarchical analysis method is a scientific method, i.e. the twowaycomparison process, the comparison of couples is the onewho reaches the relative weights of factors or Indicators andevaluation. Therefore, spatial decision-making usuallyincludes many criteria that reflect the opinions and decisionsof decision-makers, experts, interested parties andstakeholders to determine the paths of urban expansion, andbecause of these multiple opinions and decisions, all thesematters must be resolved through the adoption of a strategy ofdirect integration into the environment of information systemsprograms. The geographical representation of the results of thehierarchical analysis, which was based on the weights of allfactors, i.e. the main and sub-indicators obtained from theExpert Choice program, which worked on the integration andintegration of multi-criteria capabilities by creating anintegrated integrated unit, which proved to provide andprovide practical and scientific solutions to decision makers inthe least possible time and cost. Effort as well as its ability andcompetence to provide technical support to planning decisionmakers. Through what we mentioned in the study of assessingthe spatial suitability of urban expansion, we obtained theresults of the most appropriate, most and least appropriate forthe areas of determining the paths of urban expansion in thecity of Kirkuk.</jats:p

Pufferfish Optimization Algorithm: A New Bio-Inspired Metaheuristic Algorithm for Solving Optimization Problems

A new bio-inspired metaheuristic algorithm named the Pufferfish Optimization Algorithm (POA), that imitates the natural behavior of pufferfish in nature, is introduced in this paper. The fundamental inspiration of POA is adapted from the defense mechanism of pufferfish against predators. In this defense mechanism, by filling its elastic stomach with water, the pufferfish becomes a spherical ball with pointed spines, and as a result, the hungry predator escapes from this threat. The POA theory is stated and then mathematically modeled in two phases: (i) exploration based on the simulation of a predator’s attack on a pufferfish and (ii) exploitation based on the simulation of a predator’s escape from spiny spherical pufferfish. The performance of POA is evaluated in handling the CEC 2017 test suite for problem dimensions equal to 10, 30, 50, and 100. The optimization results show that POA has achieved an effective solution with the appropriate ability in exploration, exploitation, and the balance between them during the search process. The quality of POA in the optimization process is compared with the performance of twelve well-known metaheuristic algorithms. The simulation results show that POA provides superior performance by achieving better results in most of the benchmark functions in order to solve the CEC 2017 test suite compared to competitor algorithms. Also, the effectiveness of POA to handle optimization tasks in real-world applications is evaluated on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems. Simulation results show that POA provides effective performance in handling real-world applications by achieving better solutions compared to competitor algorithms