Heterogeneous pigeon-inspired optimization

Abstract

Pigeon-inspired optimization (PIO) is a swarm intelligence optimizer inspired by the homing behavior of pigeons. PIO consists of two optimization stages which employ the map and compass operator, and the landmark operator, respectively. In canonical PIO, these two operators treat every bird equally, which deviates from the fact that birds usually act heterogenous roles in nature. In this paper, we propose a new variant of PIO algorithm considering bird heterogeneity-HPIO. Both of the two operators are improved through dividing the birds into hub and non-hub roles. By dividing the birds into two groups, these two groups of birds are respectively assigned with different functions of "exploitation" and "exploration", so that they can closely interact with each other to locate the best promising solution. Extensive experimental studies illustrate that the bird heterogeneity produced by our algorithm can benefit the information exchange between birds so that the proposed PIO variant significantly outperforms the canonical PIO.

Hybrid Meta-Heuristic Algorithms for Optimal Sizing of Hybrid Renewable Energy System: A Review of the State-of-the-Art

“…Many new algorithms have been developed in recent years. For example Water Wave Optimization (WWO) [ 120 ], Bird Swarm (BS) [ 121 ], Equilibrium Optimizer algorithm (EO) [ 122 ], Hydrological Cycle Algorithm (HCA) [ 123 ], Farmland Fertility (FF) [ 124 ], The Sailfish Optimize (TSO) [ 125 ], Heterogeneous Pigeon-Inspired Optimization (HPIO) [ 126 ], Harris Hawks Optimization (HHO) [ 127 ], Emperor Penguins Colony (EPC) [ 128 ], Political Optimizer(PO) [ 129 ], Slime Mould Algorithm (SMA) [ 130 ] and others. The list is quickly expanding.…”

Fault diagnosis in asynchronous motors based on an optimal deep bidirectional long short-term memory networks

“…They focus on different aspects such as convergence speed, solution time, accuracy and calculation accuracy [79]. PIO as an earlier proposed swarm intelligence optimization algorithm, scholars have conducted indepth studies on it and successfully applied it in numerous fields [80]. In addition, the two parameters of the standard PIO algorithm, population size and number of iterations, will also have a great impact on the optimization effect [81], but at present, there are few literatures on the overall study of population size and number of iterations.…”

A 2020 taxonomy of algorithms inspired on living beings behavior

“…First class of animalia belongs to birds which focus on three forms of behaviors (a) Flocking or migrating, (b) Foraging or Hunting (c) Mating or breeding. Flocking behavior is a general behavior found in birds ( Bellaachia and Bari [2012]) and is found in several of them like emperador penguins increase heat radiation ( Harifi et al [2019]), migration birds ( Duman et al [2012]) or with a V configuration used in gooses ( Wang and Wang [2008]), swallows ( Neshat et al [2012]) and in pigeons for homing behavior ( Wang et al [2019], ( Duan and Qiao [2014]). A more general form of flocking is made by PSO ( Kennedy and Eberhart [1995]), and Bird Swarm ( Meng et al [2016]).…”