VIS: An artificial immune network for multi-objective optimization

Abstract

The aim of this work is to propose and validate a novel multi-objective optimization algorithm based on the emulation of the behaviour of the immune system. The rationale of this work is that the artificial immune system has, in its elementary structure, the main features required by other multi-objective evolutionary algorithms described in the literature, such as diversity preservation, memory, adaptivity, and elitism. The proposed approach is compared with three multi-objective evolutionary algorithms that are representative of the state of the art in multi-objective optimization. Algorithms are tested on six standard problems (both unconstrained and constrained) and comparisons are carried out using three different metrics. Results show that the proposed approach has very good performances and can become a valid alternative to standard algorithms for solving multi-objective optimization problems.

Water cycle algorithm for solving constrained multi-objective optimization problems

“…The chosen values for the Pareto archive size are extracted from literature [12][13][14]20] due to have fair comparisons with other optimizers in this study.…”

“…• Determine the number of streams (individuals) which flow to the rivers and sea using Eq s. (11 ) and (12 ). • Crea te randomly initi al popu lati on.…”

“…Some of these methods include the strength Pareto evolutionary algorithm (SPEA) [5], SPEA2 [6], the Pareto archive evolution strategy (PAES) [7], the micro-genetic algorithm (micro-GA) [8], the non-dominated sorting genetic algorithm (NSGA) [9], NSGA-II [10], the multi-objective particle swarm optimization (MOPSO) [11], the Pareto dominant based multi-objective simulated annealing with self-stopping criterion (PDMOSA-I) [4], the vector immune algorithm (VIS) [12], the elitist-mutation multi-objective particle swarm optimization (EM-MOPSO) [13], the weight-based multi-objective immune algorithm (WBMOIA) [14], the orthogonal simulated annealing (OSA) [15], and the hybrid quantum immune algorithm (HQIA) [16].…”

Hybrid immune algorithm with Lamarckian local search for multi-objective optimization

“…MOIA adopts a relatively complex realization of the biological immune system with a variety of variation operators such as somatic recombination, somatic mutation, gene conversion, etc. Subsequently, [16] proposed a Vector Artificial Immune System (VAIS) for solving MOPs. VAIS adopts the flowchart of an artificial immune network opt-aiNet [15] and the fitness assignment method in SPEA2 with some simplification.…”

Performance optimization algorithm of radar signal processing system

“…Artificial immune system algorithm is used to solve multi-objective optimization problem for the first time in 2002 [10]. Then many multi-objective optimization algorithms based on artificial immune system are proposed, such as MOIA [11], VIS [12][13][14], AMOBNS [15] and so on. In recent years, multi-agent system [16,17], quantum computation [18], estimation of distribution algorithm [19], fitness sharing [20], co-evolution theory [21] and differential evolution [22] are also introduced into artificial immune system algorithm.…”